id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
66033d19eed07804aec2658c8afc4f6c05b341ca430d6a4c05303edc4dc525c9 | [
"arxiv",
"semantic_scholar"
] | State Space Models are Strong Text Rerankers | Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Among these, state space models (SSMs) like Mamba offer promising advantages, particularly $O(1)$ time complexity in inference. Despite their ... | [
"Zhichao Xu",
"Jinghua Yan",
"Ashim Gupta",
"Vivek Srikumar"
] | [
"cs.CL",
"cs.IR"
] | [
"Computer Science"
] | 2024-12-18T00:00:00 | https://arxiv.org/abs/2412.14354 | https://arxiv.org/pdf/2412.14354v3 | 2412.14354 | 10.48550/arXiv.2412.14354 | 10 | 1 | false | null | Workshop on Representation Learning for NLP | 0.2603 |
804f511b6e1403d5d488acdb4c2873c9c48c696ee5c18edbb2068d23193831bb | [
"arxiv",
"semantic_scholar"
] | GG-SSMs: Graph-Generating State Space Models | State Space Models (SSMs) are powerful tools for modeling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, one-dimensional sequential processing, which restricts their ability to model non-local interactions in high-dimensional data. While methods like... | [
"Nikola Zubić",
"Davide Scaramuzza"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-12-17T00:00:00 | https://arxiv.org/abs/2412.12423 | https://arxiv.org/pdf/2412.12423v2 | 2412.12423 | 10.1109/CVPR52734.2025.02688 | 5 | 0 | false | null | Computer Vision and Pattern Recognition | 0.1945 |
59a53da5fbfcad7aec2aaecc4c403bc3706a842b4778fb833c3ad99683817acd | [
"arxiv",
"semantic_scholar"
] | BarcodeMamba: State Space Models for Biodiversity Analysis | DNA barcodes are crucial in biodiversity analysis for building automatic identification systems that recognize known species and discover unseen species. Unlike human genome modeling, barcode-based invertebrate identification poses challenges in the vast diversity of species and taxonomic complexity. Among Transformer-... | [
"Tiancheng Gao",
"Graham W. Taylor"
] | [
"cs.LG",
"q-bio.GN",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2024-12-15T00:00:00 | https://arxiv.org/abs/2412.11084 | https://arxiv.org/pdf/2412.11084v1 | 2412.11084 | 10.48550/arXiv.2412.11084 | 5 | 1 | true | https://github.com/bioscan-ml/BarcodeMamba | arXiv.org | 0.1945 |
f1896b6af59edebeaea79db208a8666e4854069cfbf5e39e37171a6136a2b1e5 | [
"arxiv",
"semantic_scholar"
] | Image Forgery Localization with State Space Models | Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Mo... | [
"Zijie Lou",
"Gang Cao",
"Kun Guo",
"Shaowei Weng",
"Lifang Yu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-12-15T00:00:00 | https://arxiv.org/abs/2412.11214 | https://arxiv.org/pdf/2412.11214v2 | 2412.11214 | 10.1109/LSP.2025.3559429 | 8 | 1 | true | https://github.com/multimediaFor/LoMa | IEEE Signal Processing Letters | 0.2386 |
0470402734e4b1aebdf2bc5823bdd36f9fb9ad40f49a463d09161fba8d3d8169 | [
"arxiv",
"semantic_scholar"
] | XYScanNet: A State Space Model for Single Image Deblurring | Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods process visual data by leveraging a flatten-and-scan strategy that converts image patches into a 1D sequence before scanning. However, this s... | [
"Hanzhou Liu",
"Chengkai Liu",
"Jiacong Xu",
"Peng Jiang",
"Mi Lu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-12-13T00:00:00 | https://arxiv.org/abs/2412.10338 | https://arxiv.org/pdf/2412.10338v3 | 2412.10338 | 10.1109/CVPRW67362.2025.00082 | 6 | 0 | false | null | Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops, 2025 | 0.2113 |
09f5453b9cfbaadf4514202ba7a0d1b597bff603a4110dd9053f70f70061b5e9 | [
"arxiv",
"semantic_scholar"
] | DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models | Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for Dynamic Graph Neural Networks (DGNNs). Dynamic Graph Structure Learning (DGSL) offers a promising way to optimize g... | [
"Haonan Yuan",
"Qingyun Sun",
"Zhaonan Wang",
"Xingcheng Fu",
"Cheng Ji",
"Yongjian Wang",
"Bo Jin",
"Jianxin Li"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-12-11T00:00:00 | https://arxiv.org/abs/2412.08160 | https://arxiv.org/pdf/2412.08160v4 | 2412.08160 | 10.48550/arXiv.2412.08160 | 15 | 2 | false | null | AAAI Conference on Artificial Intelligence | 0.301 |
b72c4346f6b58253bc9014e4cc4c82d3f312ef02ffe2ca46743793c7bbfdee90 | [
"arxiv",
"semantic_scholar"
] | Bidirectional Mamba state-space model for anomalous diffusion | Characterizing anomalous diffusion is crucial in order to understand the evolution of complex stochastic systems, from molecular interactions to cellular dynamics. In this work, we characterize the performances regarding such a task of Bi-Mamba, a novel state-space deep-learning architecture articulated with a bidirect... | [
"Maxime Lavaud",
"Yosef Shokeeb",
"Juliette Lacherez",
"Yacine Amarouchene",
"Thomas Salez"
] | [
"cond-mat.soft",
"physics.bio-ph",
"physics.optics",
"stat.ML"
] | [
"Physics",
"Mathematics"
] | 2024-12-10T00:00:00 | https://arxiv.org/abs/2412.07299 | https://arxiv.org/pdf/2412.07299v1 | 2412.07299 | 10.1088/2515-7647/add42c | 1 | 0 | false | null | null | 0.0753 |
80aa1b7f3d8b444da13b3347408b90c9a06893e10e4395d0154db3dc68764b28 | [
"arxiv",
"semantic_scholar"
] | The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity | In this paper, we analyze the computational limitations of Mamba and State-space Models (SSMs) by using the circuit complexity framework. Despite Mamba's stateful design and recent attention as a strong candidate to outperform Transformers, we have demonstrated that both Mamba and SSMs with $\mathrm{poly}(n)$-precision... | [
"Yifang Chen",
"Xiaoyu Li",
"Yingyu Liang",
"Zhenmei Shi",
"Zhao Song"
] | [
"cs.CC",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-12-09T00:00:00 | https://arxiv.org/abs/2412.06148 | https://arxiv.org/pdf/2412.06148v2 | 2412.06148 | 10.48550/arXiv.2412.06148 | 28 | 0 | false | null | null | 0.3656 |
a364113b04ffa18a3b300d12bb5bbdb1a421c9948cd95ab97652074f73dcdaf1 | [
"arxiv",
"semantic_scholar"
] | Learning Mamba as a Continual Learner: Meta-learning Selective State Space Models for Efficient Continual Learning | Continual learning (CL) aims to efficiently learn from a non-stationary data stream, without storing or recomputing all seen samples. CL enables prediction on new tasks by incorporating sequential training samples. Building on this connection between CL and sequential modeling, meta-continual learning (MCL) aims to met... | [
"Chongyang Zhao",
"Dong Gong"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-12-01T00:00:00 | https://arxiv.org/abs/2412.00776 | https://arxiv.org/pdf/2412.00776v4 | 2412.00776 | 10.48550/arXiv.2412.00776 | 4 | 0 | false | null | arXiv.org | 0.1747 |
9fb777cfe231521320735c9cddccbd19976404f2f99ae36775d341939835b338 | [
"arxiv",
"semantic_scholar"
] | In-situ observations of resident space objects with the CHEOPS space telescope | The CHaracterising ExOPlanet Satellite (CHEOPS) is a partnership between the European Space Agency and Switzerland with important contributions by 10 additional ESA member States. It is the first S-class mission in the ESA Science Programme. CHEOPS has been flying on a Sun-synchronous low Earth orbit since December 201... | [
"Nicolas Billot",
"Stephan Hellmich",
"Willy Benz",
"Andrea Fortier",
"David Ehrenreich",
"Christopher Broeg",
"Alexis Heitzmann",
"Anja Bekkelien",
"Alexis Brandeker",
"Yann Alibert",
"Roi Alonso",
"Tamas Bárczy",
"David Barrado Navascues",
"Susana C. C. Barros",
"Wolfgang Baumjohann",
... | [
"astro-ph.EP",
"astro-ph.IM",
"physics.data-an",
"physics.space-ph"
] | [
"Physics"
] | 2024-11-27T00:00:00 | https://arxiv.org/abs/2411.18326 | https://arxiv.org/pdf/2411.18326v1 | 2411.18326 | 10.1016/j.jsse.2024.08.005 | 3 | 0 | false | null | Journal of Space Safety Engineering | 0.1505 |
f07dded78933fbf41fdef11fb3ed23e843e8e7c282ee8c3526f5aa559bd1dae7 | [
"arxiv",
"semantic_scholar"
] | Deformable Mamba for Wide Field of View Segmentation | Recent advancements in the Mamba architecture, with its linear computational complexity, being a promising alternative to transformer architectures suffering from quadratic complexity. While existing works primarily focus on adapting Mamba as vision encoders, the critical role of task-specific Mamba decoders remains un... | [
"Jie Hu",
"Junwei Zheng",
"Jiale Wei",
"Jiaming Zhang",
"Rainer Stiefelhagen"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-11-25T00:00:00 | https://arxiv.org/abs/2411.16481 | https://arxiv.org/pdf/2411.16481v2 | 2411.16481 | 10.48550/arXiv.2411.16481 | 10 | 0 | true | https://github.com/JieHu1996/DeformableMamba | arXiv.org | 0.2603 |
9ea46fbfef41aa89eb99eb2d81c1ed1939388185e7224909fac6d458dba918cc | [
"arxiv",
"semantic_scholar"
] | Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning | Continual Learning (CL) aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. Recently, State Space Models (SSMs), particularly the Mamba model, have achieved notable success in computer vision. Building on the strengths of SSMs, this study exp... | [
"De Cheng",
"Yue Lu",
"Lingfeng He",
"Shizhou Zhang",
"Xi Yang",
"Nannan Wang",
"Xinbo Gao"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-11-23T00:00:00 | https://arxiv.org/abs/2411.15469 | https://arxiv.org/pdf/2411.15469v2 | 2411.15469 | 10.48550/arXiv.2411.15469 | 5 | 1 | true | null | arXiv.org | 0.1945 |
e4b54cfe1481fa04ea0cbb5ccb7d62d0cc210bd476de0fb8afeb4ffe65061a98 | [
"arxiv",
"semantic_scholar"
] | EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality | For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively. Recently, the state space model (SSM) has emerged as an effective operation for global interaction with i... | [
"Sanghyeok Lee",
"Joonmyung Choi",
"Hyunwoo J. Kim"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-11-22T00:00:00 | https://arxiv.org/abs/2411.15241 | https://arxiv.org/pdf/2411.15241v2 | 2411.15241 | 10.1109/CVPR52734.2025.01390 | 48 | 2 | true | https://github.com/mlvlab/EfficientViM | Computer Vision and Pattern Recognition | 0.4225 |
0d91ad0de8967a714ca4ef8862b516f89d10d15efe602c286e21a14574693763 | [
"arxiv",
"semantic_scholar"
] | Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation | Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain largely unexplored. In our study, we introduce two key insights-driven strategies for PEFT in Mamba architecture: (1) While state-space models (S... | [
"Seokil Ham",
"Hee-Seon Kim",
"Sangmin Woo",
"Changick Kim"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-21T00:00:00 | https://arxiv.org/abs/2411.15224 | https://arxiv.org/pdf/2411.15224v3 | 2411.15224 | 10.1109/CVPR52734.2025.02802 | 3 | 1 | false | null | Computer Vision and Pattern Recognition | 0.1505 |
aee5ad61cb745d34fdba011f574e2a659d3a9eb18d45eed2f615eb8d4c450910 | [
"arxiv",
"semantic_scholar"
] | Bi-Mamba: Towards Accurate 1-Bit State Space Models | The typical Selective State-Space Model (SSM) used in Mamba addresses several limitations of Transformers, such as the quadratic computational complexity with respect to sequence length and the significant memory requirements during inference due to the key-value (KV) cache. However, the increasing size of Mamba models... | [
"Shengkun Tang",
"Liqun Ma",
"Haonan Li",
"Mingjie Sun",
"Zhiqiang Shen"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11843 | https://arxiv.org/pdf/2411.11843v2 | 2411.11843 | 10.48550/arXiv.2411.11843 | 10 | 2 | true | https://github.com/Tangshengku/Bi-Mamba | arXiv.org | 0.2603 |
c8094b1ee4e37bd3f4b21b4c1be350d7d2b120b0ed828b0b1c0ab595dcc7ad7a | [
"arxiv",
"semantic_scholar"
] | KAN-Mamba FusionNet: Redefining Medical Image Segmentation with Non-Linear Modeling | Medical image segmentation is essential for applications like robotic surgeries, disease diagnosis, and treatment planning. Recently, various deep-learning models have been proposed to enhance medical image segmentation. One promising approach utilizes Kolmogorov-Arnold Networks (KANs), which better capture non-lineari... | [
"Akansh Agrawal",
"Akshan Agrawal",
"Shashwat Gupta",
"Priyanka Bagade"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11926 | https://arxiv.org/pdf/2411.11926v2 | 2411.11926 | 10.48550/arXiv.2411.11926 | 9 | 3 | false | null | arXiv.org | 0.301 |
3767d8d16bc568373843622246713a8b277f9ee05271650c27dd883ea497749f | [
"arxiv",
"semantic_scholar"
] | $\text{S}^{3}$Mamba: Arbitrary-Scale Super-Resolution via Scaleable State Space Model | Arbitrary scale super-resolution (ASSR) aims to super-resolve low-resolution images to high-resolution images at any scale using a single model, addressing the limitations of traditional super-resolution methods that are restricted to fixed-scale factors (e.g., $\times2$, $\times4$). The advent of Implicit Neural Repre... | [
"Peizhe Xia",
"Long Peng",
"Xin Di",
"Renjing Pei",
"Yang Wang",
"Yang Cao",
"Zheng-Jun Zha"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-11-16T00:00:00 | https://arxiv.org/abs/2411.11906 | https://arxiv.org/pdf/2411.11906v1 | 2411.11906 | 10.48550/arXiv.2411.11906 | 14 | 0 | false | null | arXiv.org | 0.294 |
bf1bc416a65a0890cfb02dadc071e2f8fcab1de380e52f8babbcf7982504d27d | [
"arxiv",
"semantic_scholar"
] | XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection | Transformers and their variants have achieved great success in speech processing. However, their multi-head self-attention mechanism is computationally expensive. Therefore, one novel selective state space model, Mamba, has been proposed as an alternative. Building on its success in automatic speech recognition, we app... | [
"Yang Xiao",
"Rohan Kumar Das"
] | [
"eess.AS",
"cs.SD"
] | [
"Computer Science",
"Engineering"
] | 2024-11-15T00:00:00 | https://arxiv.org/abs/2411.10027 | https://arxiv.org/pdf/2411.10027v2 | 2411.10027 | 10.1109/LSP.2025.3547861 | 54 | 8 | true | https://github.com/swagshaw/XLSR-Mamba | IEEE Signal Processing Letters | 0.4771 |
448da04f27c8b3b3b8e8c08a9ec58d18bf68cd5fac834934d4d81e018ea223e2 | [
"arxiv",
"semantic_scholar"
] | CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising | Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-range modeling capabilities, while Transformer-based denoising methods,... | [
"Linxuan Li",
"Wenjia Wei",
"Luyao Yang",
"Wenwen Zhang",
"Jiashu Dong",
"Yahua Liu",
"Hongshi Huang",
"Wei Zhao"
] | [
"eess.IV"
] | [
"Medicine",
"Computer Science",
"Engineering"
] | 2024-11-12T00:00:00 | https://arxiv.org/abs/2411.07930 | https://arxiv.org/pdf/2411.07930v5 | 2411.07930 | 10.1016/j.compmedimag.2025.102595 | 23 | 0 | false | null | null | 0.3451 |
8e4e1f5f8bde36405d7e5993c77f6a313f31b383d4daca5ef489127440f78ff0 | [
"arxiv",
"semantic_scholar"
] | Mamba-based Decoder-Only Approach with Bidirectional Speech Modeling for Speech Recognition | Selective state space models (SSMs) represented by Mamba have demonstrated their computational efficiency and promising outcomes in various tasks, including automatic speech recognition (ASR). Mamba has been applied to ASR task with the attention-based encoder-decoder framework, where the cross-attention mechanism betw... | [
"Yoshiki Masuyama",
"Koichi Miyazaki",
"Masato Murata"
] | [
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-11-11T00:00:00 | https://arxiv.org/abs/2411.06968 | https://arxiv.org/pdf/2411.06968v1 | 2411.06968 | 10.1109/SLT61566.2024.10832186 | 7 | 0 | false | null | Spoken Language Technology Workshop | 0.2258 |
7c238c91ebdaa4abc1ad7d5273bff138dd34d9cd18340b5d5b4cddcff85ba853 | [
"arxiv",
"semantic_scholar"
] | SEM-Net: Efficient Pixel Modelling for image inpainting with Spatially Enhanced SSM | Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. \revise{Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhibit similar patterns to the semanticly consistent regions}. Th... | [
"Shuang Chen",
"Haozheng Zhang",
"Amir Atapour-Abarghouei",
"Hubert P. H. Shum"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-11-10T00:00:00 | https://arxiv.org/abs/2411.06318 | https://arxiv.org/pdf/2411.06318v1 | 2411.06318 | 10.1109/WACV61041.2025.00055 | 18 | 2 | true | https://github.com/ChrisChen1023/SEM-Net | IEEE Workshop/Winter Conference on Applications of Computer Vision | 0.3197 |
3c8e10cb933c78eb58d2e40ab8e2f8d7db5d5bc764e26f7f189abefbf68bd091 | [
"arxiv",
"semantic_scholar"
] | MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba | An ecosystem of Transformer-based models has been established by building large models with extensive data. Parameter-efficient fine-tuning (PEFT) is a crucial technology for deploying these models to downstream tasks with minimal cost while achieving effective performance. Recently, Mamba, a State Space Model (SSM)-ba... | [
"Masakazu Yoshimura",
"Teruaki Hayashi",
"Yota Maeda"
] | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-06T00:00:00 | https://arxiv.org/abs/2411.03855 | https://arxiv.org/pdf/2411.03855v3 | 2411.03855 | 10.48550/arXiv.2411.03855 | 14 | 2 | false | null | International Conference on Learning Representations | 0.294 |
4a3ec255a9bce206f13e97fe58871402bdb038a078869919256f4cf5cfc9b99d | [
"arxiv",
"semantic_scholar"
] | A Mamba Foundation Model for Time Series Forecasting | Time series foundation models have demonstrated strong performance in zero-shot learning, making them well-suited for predicting rapidly evolving patterns in real-world applications where relevant training data are scarce. However, most of these models rely on the Transformer architecture, which incurs quadratic comple... | [
"Haoyu Ma",
"Yushu Chen",
"Wenlai Zhao",
"Jinzhe Yang",
"Yingsheng Ji",
"Xinghua Xu",
"Xiaozhu Liu",
"Hao Jing",
"Shengzhuo Liu",
"Guangwen Yang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-05T00:00:00 | https://arxiv.org/abs/2411.02941 | https://arxiv.org/pdf/2411.02941v1 | 2411.02941 | 10.48550/arXiv.2411.02941 | 18 | 0 | false | null | arXiv.org | 0.3197 |
b698d7e572c863c8b8a2f5528ddca2ce02a0b61acd531eac2bc2aeb664a4d1eb | [
"arxiv",
"semantic_scholar"
] | NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs | Transformers have become dominant in large-scale deep learning tasks across various domains, including text, 2D and 3D vision. However, the quadratic complexity of their attention mechanism limits their efficiency as the sequence length increases, particularly in high-resolution 3D data such as point clouds. Recently, ... | [
"Nursena Köprücü",
"Destiny Okpekpe",
"Antonio Orvieto"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-31T00:00:00 | https://arxiv.org/abs/2411.00151 | https://arxiv.org/pdf/2411.00151v1 | 2411.00151 | 10.48550/arXiv.2411.00151 | 3 | 1 | false | null | arXiv.org | 0.1505 |
fca274ebcba001581dd8f5bef1b09cfd8d629e3cd9d60c4dfd119e86d9711e9c | [
"arxiv",
"semantic_scholar"
] | Sequential Order-Robust Mamba for Time Series Forecasting | Mamba has recently emerged as a promising alternative to Transformers, offering near-linear complexity in processing sequential data. However, while channels in time series (TS) data have no specific order in general, recent studies have adopted Mamba to capture channel dependencies (CD) in TS, introducing a sequential... | [
"Seunghan Lee",
"Juri Hong",
"Kibok Lee",
"Taeyoung Park"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.23356 | https://arxiv.org/pdf/2410.23356v1 | 2410.23356 | 10.48550/arXiv.2410.23356 | 3 | 0 | true | https://github.com/seunghan96/SOR-Mamba | arXiv.org | 0.1505 |
3c5f542080532847c5775814d89cfd37d09dc49d19c320c740deccc69f1b2283 | [
"arxiv",
"semantic_scholar"
] | ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction | Exposure Correction (EC) aims to recover proper exposure conditions for images captured under over-exposure or under-exposure scenarios. While existing deep learning models have shown promising results, few have fully embedded Retinex theory into their architecture, highlighting a gap in current methodologies. Addition... | [
"Wei Dong",
"Han Zhou",
"Yulun Zhang",
"Xiaohong Liu",
"Jun Chen"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-10-28T00:00:00 | https://arxiv.org/abs/2410.21535 | https://arxiv.org/pdf/2410.21535v1 | 2410.21535 | 10.48550/arXiv.2410.21535 | 23 | 1 | true | https://github.com/LowlevelAI/ECMamba | Neural Information Processing Systems | 0.3451 |
3642fd96b8ae029569c252917d163d0282b05e69983ff46b51626cc63a2a1124 | [
"arxiv",
"semantic_scholar"
] | Revealing and Mitigating the Local Pattern Shortcuts of Mamba | Large language models (LLMs) have advanced significantly due to the attention mechanism, but their quadratic complexity and linear memory demands limit their performance on long-context tasks. Recently, researchers introduced Mamba, an advanced model built upon State Space Models(SSMs) that offers linear complexity and... | [
"Wangjie You",
"Zecheng Tang",
"Juntao Li",
"Lili Yao",
"Min Zhang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-21T00:00:00 | https://arxiv.org/abs/2410.15678 | https://arxiv.org/pdf/2410.15678v1 | 2410.15678 | 10.48550/arXiv.2410.15678 | 1 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.0753 |
1a8113b6def333a98af2ac79ab4e1e7bae7a660ffffe893c8d9db0f471f88abf | [
"arxiv",
"semantic_scholar"
] | R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation | Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly utilizes Transformers to generateradiology reports, which can be computat... | [
"Yongheng Sun",
"Yueh Z. Lee",
"Genevieve A. Woodard",
"Hongtu Zhu",
"Chunfeng Lian",
"Mingxia Liu"
] | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-21T00:00:00 | https://arxiv.org/abs/2410.18135 | https://arxiv.org/pdf/2410.18135v1 | 2410.18135 | 10.1109/ISBI60581.2025.10980814 | 19 | 3 | false | null | IEEE International Symposium on Biomedical Imaging | 0.3253 |
305a4bc32bbf7d3e3281fef68d8bf393b8fd1eeec36458f456baae82fd746297 | [
"arxiv",
"semantic_scholar"
] | Spatial-Mamba: Effective Visual State Space Models via Structure-aware State Fusion | Selective state space models (SSMs), such as Mamba, highly excel at capturing long-range dependencies in 1D sequential data, while their applications to 2D vision tasks still face challenges. Current visual SSMs often convert images into 1D sequences and employ various scanning patterns to incorporate local spatial dep... | [
"Chaodong Xiao",
"Minghan Li",
"Zhengqiang Zhang",
"Deyu Meng",
"Lei Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-10-19T00:00:00 | https://arxiv.org/abs/2410.15091 | https://arxiv.org/pdf/2410.15091v2 | 2410.15091 | 10.48550/arXiv.2410.15091 | 57 | 6 | true | https://github.com/EdwardChasel/Spatial-Mamba | International Conference on Learning Representations | 0.4409 |
a63bc2d6dac9e1c748e5df4b1ecbabe0863cab6e186ede0d5bd7963d1b51977a | [
"arxiv",
"semantic_scholar"
] | Quamba: A Post-Training Quantization Recipe for Selective State Space Models | State Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity which allows for holding longer context lengths than attention-based networks. The superior computational efficiency of SSMs in long sequence ... | [
"Hung-Yueh Chiang",
"Chi-Chih Chang",
"Natalia Frumkin",
"Kai-Chiang Wu",
"Diana Marculescu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-17T00:00:00 | https://arxiv.org/abs/2410.13229 | https://arxiv.org/pdf/2410.13229v2 | 2410.13229 | 10.48550/arXiv.2410.13229 | 22 | 3 | false | null | arXiv.org | 0.3404 |
5b8fbaaff66e40afcc0bcb45882e0b1eea9276eb9eca876e1cae38c9ab1f5bc7 | [
"arxiv",
"semantic_scholar"
] | Provable Benefits of Complex Parameterizations for Structured State Space Models | Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. T... | [
"Yuval Ran-Milo",
"Eden Lumbroso",
"Edo Cohen-Karlik",
"Raja Giryes",
"Amir Globerson",
"Nadav Cohen"
] | [
"cs.LG",
"cs.AI",
"cs.NE"
] | [
"Computer Science"
] | 2024-10-17T00:00:00 | https://arxiv.org/abs/2410.14067 | https://arxiv.org/pdf/2410.14067v2 | 2410.14067 | 10.48550/arXiv.2410.14067 | 11 | 0 | false | null | Neural Information Processing Systems | 0.2698 |
572e60c2e3b7c8e373c18a3a851edd69362f22b52c4f2faea68f8e322261f177 | [
"arxiv",
"semantic_scholar"
] | Rethinking Token Reduction for State Space Models | Recent advancements in State Space Models (SSMs) have attracted significant interest, particularly in models optimized for parallel training and handling long-range dependencies. Architectures like Mamba have scaled to billions of parameters with selective SSM. To facilitate broader applications using Mamba, exploring ... | [
"Zheng Zhan",
"Yushu Wu",
"Zhenglun Kong",
"Changdi Yang",
"Yifan Gong",
"Xuan Shen",
"Xue Lin",
"Pu Zhao",
"Yanzhi Wang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-10-16T00:00:00 | https://arxiv.org/abs/2410.14725 | https://arxiv.org/pdf/2410.14725v1 | 2410.14725 | 10.48550/arXiv.2410.14725 | 20 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3306 |
53bf7b9c976e4206c411e8fe038b873aaa425541d39ad801ea8aa44411b95720 | [
"arxiv",
"semantic_scholar"
] | Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks | Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient descent. We propose a sufficient constructive inductive bias for LRNNs: equip a diagonal recurrent state with multiplicative readout and a s... | [
"Yudou Tian",
"Neeraj Mohan Sushma",
"Harshvardhan Mestha",
"Nicolo Colombo",
"David Kappel",
"Anand Subramoney"
] | [
"cs.LG",
"cs.AI",
"cs.NE"
] | [
"Computer Science"
] | 2024-10-15T00:00:00 | https://arxiv.org/abs/2410.11687 | https://arxiv.org/pdf/2410.11687v3 | 2410.11687 | null | 4 | 0 | false | null | null | 0.1747 |
c50bc147b4ad5018a145d768021ad1203bfc4f332a5523bdd749a2813e211ef2 | [
"arxiv",
"semantic_scholar"
] | UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba | Multivariate Time series forecasting is crucial in domains such as transportation, meteorology, and finance, especially for predicting extreme weather events. State-of-the-art methods predominantly rely on Transformer architectures, which utilize attention mechanisms to capture temporal dependencies. However, these met... | [
"Li Wu",
"Wenbin Pei",
"Jiulong Jiao",
"Qiang Zhang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-10-15T00:00:00 | https://arxiv.org/abs/2410.11278 | https://arxiv.org/pdf/2410.11278v1 | 2410.11278 | 10.48550/arXiv.2410.11278 | 5 | 0 | false | null | arXiv.org | 0.1945 |
f5a9a532802c9e5e0e51bfdd564e58386bc4749f93b6cf6f49ea601a2135df1e | [
"arxiv",
"semantic_scholar"
] | Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution | State Space Models (SSM), such as Mamba, have shown strong representation ability in modeling long-range dependency with linear complexity, achieving successful applications from high-level to low-level vision tasks. However, SSM's sequential nature necessitates multiple scans in different directions to compensate for ... | [
"Junbo Qiao",
"Jincheng Liao",
"Wei Li",
"Yulun Zhang",
"Yong Guo",
"Yi Wen",
"Zhangxizi Qiu",
"Jiao Xie",
"Jie Hu",
"Shaohui Lin"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10140 | https://arxiv.org/pdf/2410.10140v1 | 2410.10140 | 10.48550/arXiv.2410.10140 | 21 | 0 | false | null | arXiv.org | 0.3356 |
43557ac2ae2c86db30dde1586873728a220e8fa5c1ec30d4e68ff608c80abd11 | [
"arxiv",
"semantic_scholar"
] | V2M: Visual 2-Dimensional Mamba for Image Representation Learning | Mamba has garnered widespread attention due to its flexible design and efficient hardware performance to process 1D sequences based on the state space model (SSM). Recent studies have attempted to apply Mamba to the visual domain by flattening 2D images into patches and then regarding them as a 1D sequence. To compensa... | [
"Chengkun Wang",
"Wenzhao Zheng",
"Yuanhui Huang",
"Jie Zhou",
"Jiwen Lu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10382 | https://arxiv.org/pdf/2410.10382v1 | 2410.10382 | 10.48550/arXiv.2410.10382 | 8 | 1 | false | null | arXiv.org | 0.2386 |
46d882d52ac331aa47b5845acbfc010ca9e62dfa05c46b10cb3761e7ba0cff30 | [
"arxiv",
"semantic_scholar"
] | Parameter-Efficient Fine-Tuning of State Space Models | Deep State Space Models (SSMs), such as Mamba (Gu & Dao, 2024), have become powerful tools for language modeling, offering high performance and linear scalability with sequence length. However, the application of parameter-efficient fine-tuning (PEFT) methods to SSM-based models remains largely underexplored. We start ... | [
"Kevin Galim",
"Wonjun Kang",
"Yuchen Zeng",
"Hyung Il Koo",
"Kangwook Lee"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-10-11T00:00:00 | https://arxiv.org/abs/2410.09016 | https://arxiv.org/pdf/2410.09016v3 | 2410.09016 | 10.48550/arXiv.2410.09016 | 10 | 0 | true | https://github.com/furiosa-ai/ssm-peft | International Conference on Machine Learning | 0.2603 |
f6e7736a10f67cae2ad026c9205e00e101a85cc77119ffe18d419c8727d5fcb6 | [
"arxiv",
"semantic_scholar"
] | Mamba-based Segmentation Model for Speaker Diarization | Mamba is a newly proposed architecture which behaves like a recurrent neural network (RNN) with attention-like capabilities. These properties are promising for speaker diarization, as attention-based models have unsuitable memory requirements for long-form audio, and traditional RNN capabilities are too limited. In thi... | [
"Alexis Plaquet",
"Naohiro Tawara",
"Marc Delcroix",
"Shota Horiguchi",
"Atsushi Ando",
"Shoko Araki"
] | [
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-10-09T00:00:00 | https://arxiv.org/abs/2410.06459 | https://arxiv.org/pdf/2410.06459v2 | 2410.06459 | 10.1109/ICASSP49660.2025.10889446 | 14 | 3 | true | https://github.com/nttcslab-sp/mamba-diarization | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.301 |
0bea78d60309f6bb23ce7c8d8a391cff963811e12376e763cb346d2e84533b38 | [
"arxiv",
"semantic_scholar"
] | Stuffed Mamba: Oversized States Lead to the Inability to Forget | Recent advancements in recurrent architectures, such as Mamba and RWKV, have showcased strong language capabilities. Unlike transformer-based models, these architectures encode all contextual information into a fixed-size state, leading to great inference efficiency. However, this approach can cause information interfe... | [
"Yingfa Chen",
"Xinrong Zhang",
"Shengding Hu",
"Xu Han",
"Zhiyuan Liu",
"Maosong Sun"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-09T00:00:00 | https://arxiv.org/abs/2410.07145 | https://arxiv.org/pdf/2410.07145v4 | 2410.07145 | null | 5 | 0 | false | null | null | 0.1945 |
3386cc07fdee2dd428959f7a8eedf08f104337cad9b13480a790ae9ba30abc49 | [
"arxiv",
"semantic_scholar"
] | TIMBA: Time series Imputation with Bi-directional Mamba Blocks and Diffusion models | The problem of imputing multivariate time series spans a wide range of fields, from clinical healthcare to multi-sensor systems. Initially, Recurrent Neural Networks (RNNs) were employed for this task; however, their error accumulation issues led to the adoption of Transformers, leveraging attention mechanisms to mitig... | [
"Javier Solís-García",
"Belén Vega-Márquez",
"Juan A. Nepomuceno",
"Isabel A. Nepomuceno-Chamorro"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-10-08T00:00:00 | https://arxiv.org/abs/2410.05916 | https://arxiv.org/pdf/2410.05916v1 | 2410.05916 | 10.48550/arXiv.2410.05916 | 2 | 0 | false | null | arXiv.org | 0.1193 |
fc389c5bd724f137d670305b0b4d4b70354a2b1ac8d0991bf02ea260b310bca9 | [
"arxiv",
"semantic_scholar"
] | SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning | Spiking neural networks (SNNs) provide an energy-efficient solution by utilizing the spike-based and sparse nature of biological systems. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on long sequential tasks, until the recent emergence of state space models (SSMs), which off... | [
"Yan Zhong",
"Ruoyu Zhao",
"Chao Wang",
"Qinghai Guo",
"Jianguo Zhang",
"Zhichao Lu",
"Luziwei Leng"
] | [
"cs.NE",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-07T00:00:00 | https://arxiv.org/abs/2410.17268 | https://arxiv.org/pdf/2410.17268v1 | 2410.17268 | 10.48550/arXiv.2410.17268 | 12 | 1 | false | null | arXiv.org | 0.2785 |
226b2e8e0c5dc464fb4b60bbf95a5cf09117054fbfb29142a08413494e6b817c | [
"arxiv",
"semantic_scholar"
] | Falcon Mamba: The First Competitive Attention-free 7B Language Model | In this technical report, we present Falcon Mamba 7B, a new base large language model based on the novel Mamba architecture. Falcon Mamba 7B is trained on 5.8 trillion tokens with carefully selected data mixtures. As a pure Mamba-based model, Falcon Mamba 7B surpasses leading open-weight models based on Transformers, s... | [
"Jingwei Zuo",
"Maksim Velikanov",
"Dhia Eddine Rhaiem",
"Ilyas Chahed",
"Younes Belkada",
"Guillaume Kunsch",
"Hakim Hacid"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-07T00:00:00 | https://arxiv.org/abs/2410.05355 | https://arxiv.org/pdf/2410.05355v1 | 2410.05355 | 10.48550/arXiv.2410.05355 | 47 | 6 | false | null | arXiv.org | 0.4225 |
0a74129afdb790f4a7031b2c7a435f07a1cd171b17c818976493beb49d4afb7a | [
"arxiv",
"semantic_scholar"
] | Exploring the Limitations of Mamba in COPY and CoT Reasoning | Transformers have become the backbone of modern Large Language Models (LLMs); however, their inference overhead grows linearly with the sequence length, posing challenges for modeling long sequences. In light of this, Mamba has attracted attention for maintaining a constant inference size, with empirical evidence demon... | [
"Ruifeng Ren",
"Zhicong Li",
"Yong Liu"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03810 | https://arxiv.org/pdf/2410.03810v3 | 2410.03810 | 10.18653/v1/2025.emnlp-main.634 | 7 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2258 |
0c3bc38f38ba8c4d92b151c17bdf91b339e75d260a92e363fb9673dc4272bd61 | [
"arxiv",
"semantic_scholar"
] | Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation | Writing stories is an engaging yet challenging endeavor. Often, authors encounter moments of creative block, where the path forward in their narrative becomes obscured. This paper is designed to address such moments by providing an innovative solution: A tool that completes stories based on given prompts. By inputting ... | [
"Divyam Sharma",
"Divya Santhanam"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.10848 | https://arxiv.org/pdf/2410.10848v1 | 2410.10848 | 10.48550/arXiv.2410.10848 | 0 | 0 | true | null | arXiv.org | 0 |
e8fe853bf9a8472b2756eeeab5192dd168a14b87545d4988b6b453a9cdff04da | [
"arxiv",
"semantic_scholar"
] | Mamba in Vision: A Comprehensive Survey of Techniques and Applications | Mamba is emerging as a novel approach to overcome the challenges faced by Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision. While CNNs excel at extracting local features, they often struggle to capture long-range dependencies without complex architectural modifications. In contrast... | [
"Md Maklachur Rahman",
"Abdullah Aman Tutul",
"Ankur Nath",
"Lamyanba Laishram",
"Soon Ki Jung",
"Tracy Hammond"
] | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03105 | https://arxiv.org/pdf/2410.03105v1 | 2410.03105 | 10.48550/arXiv.2410.03105 | 45 | 0 | true | https://github.com/maklachur/Mamba-in-Computer-Vision | arXiv.org | 0.4157 |
99e786f08a1bfe81b1ace0985bc07dbec8939417a45cec22fc6fcc1ebc4760b5 | [
"arxiv",
"semantic_scholar"
] | Demystifying the Token Dynamics of Deep Selective State Space Models | Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains elusive, hindering their further development and adoption for applicatio... | [
"Thieu N Vo",
"Tung D. Pham",
"Xin T. Tong",
"Tan Minh Nguyen"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03292 | https://arxiv.org/pdf/2410.03292v2 | 2410.03292 | 10.48550/arXiv.2410.03292 | 1 | 0 | false | null | International Conference on Learning Representations | 0.0753 |
a245c895b30faed6264f50c815e23ece06aac54a9cafe317d8562774bf0be7ca | [
"arxiv",
"semantic_scholar"
] | Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs | Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have emerged as the preferred architecture for PDEs due to their ability to capture intricate dependencies. However, they struggle with representi... | [
"Chun-Wun Cheng",
"Jiahao Huang",
"Yi Zhang",
"Guang Yang",
"Carola-Bibiane Schönlieb",
"Angelica I. Aviles-Rivero"
] | [
"cs.LG",
"math.NA"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-03T00:00:00 | https://arxiv.org/abs/2410.02113 | https://arxiv.org/pdf/2410.02113v3 | 2410.02113 | 10.48550/arXiv.2410.02113 | 16 | 0 | true | https://github.com/Math-ML-X/Mamba-Neural-Operator | Journal of Computational Physics | 0.3076 |
6a79d1a32febd4b0039d91dc3f2fd091a611896edffcf74536aed6f36d3d5b2b | [
"arxiv",
"semantic_scholar"
] | A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond | Mamba, a special case of the State Space Model, is gaining popularity as an alternative to template-based deep learning approaches in medical image analysis. While transformers are powerful architectures, they have drawbacks, including quadratic computational complexity and an inability to address long-range dependenci... | [
"Shubhi Bansal",
"Sreeharish A",
"Madhava Prasath J",
"Manikandan S",
"Sreekanth Madisetty",
"Mohammad Zia Ur Rehman",
"Chandravardhan Singh Raghaw",
"Gaurav Duggal",
"Nagendra Kumar"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-03T00:00:00 | https://arxiv.org/abs/2410.02362 | https://arxiv.org/pdf/2410.02362v3 | 2410.02362 | 10.48550/arXiv.2410.02362 | 30 | 0 | false | null | arXiv.org | 0.3728 |
f6f366010c26a24780e54ef9ca12088f2ccdacafe6485dc6e27fbae48e1bc3c3 | [
"arxiv",
"semantic_scholar"
] | A SSM is Polymerized from Multivariate Time Series | For multivariate time series (MTS) tasks, previous state space models (SSMs) followed the modeling paradigm of Transformer-based methods. However, none of them explicitly model the complex dependencies of MTS: the Channel Dependency variations with Time (CDT). In view of this, we delve into the derivation of SSM, which... | [
"Haixiang Wu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-09-30T00:00:00 | https://arxiv.org/abs/2409.20310 | https://arxiv.org/pdf/2409.20310v2 | 2409.20310 | 10.48550/arXiv.2409.20310 | 0 | 0 | true | https://github.com/Joeland4/Poly-Mamba | arXiv.org | 0 |
655da328920363e9300936f0e98629ec795944f7b3a7ef16f0fd5cb1f050f984 | [
"arxiv",
"semantic_scholar"
] | Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models | Current automatic speech recognition systems struggle with modeling long speech sequences due to high quadratic complexity of Transformer-based models. Selective state space models such as Mamba has performed well on long-sequence modeling in natural language processing and computer vision tasks. However, research ende... | [
"Xiaoxue Gao",
"Nancy F. Chen"
] | [
"eess.AS",
"cs.SD"
] | [
"Engineering",
"Computer Science"
] | 2024-09-27T00:00:00 | https://arxiv.org/abs/2409.18654 | https://arxiv.org/pdf/2409.18654v1 | 2409.18654 | 10.1109/SLT61566.2024.10832137 | 14 | 1 | false | null | Spoken Language Technology Workshop | 0.294 |
7ffb067db96e49d7c899100bb4e265ecd055bb673ce3fa3e29db5545aeb72bbe | [
"arxiv",
"semantic_scholar"
] | DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection | Depression is a common mental disorder that affects millions of people worldwide. Although promising, current multimodal methods hinge on aligned or aggregated multimodal fusion, suffering two significant limitations: (i) inefficient long-range temporal modeling, and (ii) sub-optimal multimodal fusion between intermoda... | [
"Jiaxin Ye",
"Junping Zhang",
"Hongming Shan"
] | [
"cs.CY",
"cs.CV",
"cs.HC"
] | [
"Computer Science"
] | 2024-09-24T00:00:00 | https://arxiv.org/abs/2409.15936 | https://arxiv.org/pdf/2409.15936v1 | 2409.15936 | 10.1109/ICASSP49660.2025.10889975 | 36 | 1 | true | https://github.com/Jiaxin-Ye/DepMamba | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.3921 |
1da622b2a2dde97ed8837fec5c38996e7d610a32754bc660562fb1e3b7849f0b | [
"arxiv",
"semantic_scholar"
] | DiSPo: Diffusion-SSM based Policy Learning for Coarse-to-Fine Action Discretization | We aim to solve the problem of generating coarse-to-fine skills learning from demonstrations (LfD). To scale precision, traditional LfD approaches often rely on extensive fine-grained demonstrations with external interpolations or dynamics models with limited generalization capabilities. For memory-efficient learning a... | [
"Nayoung Oh",
"Jaehyeong Jang",
"Moonkyeong Jung",
"Daehyung Park"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-09-23T00:00:00 | https://arxiv.org/abs/2409.14719 | https://arxiv.org/pdf/2409.14719v4 | 2409.14719 | 10.48550/arXiv.2409.14719 | 4 | 0 | false | null | arXiv.org | 0.1747 |
32b24b10152b1ea843de26f0e4a224b4261aa3b117f42ada8836c59aa1edc90d | [
"arxiv",
"semantic_scholar"
] | Topological Deep Learning with State-Space Models: A Mamba Approach for Simplicial Complexes | Graph Neural Networks based on the message-passing (MP) mechanism are a dominant approach for handling graph-structured data. However, they are inherently limited to modeling only pairwise interactions, making it difficult to explicitly capture the complexity of systems with $n$-body relations. To address this, topolog... | [
"Marco Montagna",
"Simone Scardapane",
"Lev Telyatnikov"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-09-18T00:00:00 | https://arxiv.org/abs/2409.12033 | https://arxiv.org/pdf/2409.12033v1 | 2409.12033 | 10.1109/IJCNN64981.2025.11227272 | 2 | 0 | false | null | IEEE International Joint Conference on Neural Network | 0.1193 |
86ed9c0e2804c95e75cca8c6a2aae4e533dcfca6dd937a94ea98f888838a0448 | [
"arxiv",
"semantic_scholar"
] | Bio-Inspired Mamba: Temporal Locality and Bioplausible Learning in Selective State Space Models | This paper introduces Bio-Inspired Mamba (BIM), a novel online learning framework for selective state space models that integrates biological learning principles with the Mamba architecture. BIM combines Real-Time Recurrent Learning (RTRL) with Spike-Timing-Dependent Plasticity (STDP)-like local learning rules, address... | [
"Jiahao Qin"
] | [
"cs.NE",
"cs.CL"
] | [
"Computer Science"
] | 2024-09-17T00:00:00 | https://arxiv.org/abs/2409.11263 | https://arxiv.org/pdf/2409.11263v1 | 2409.11263 | 10.48550/arXiv.2409.11263 | 2 | 1 | false | null | arXiv.org | 0.1505 |
6deb9b6231ac4f31a9cb581dbcc075c276b322014db84f16b16330f000d67c86 | [
"arxiv",
"semantic_scholar"
] | Mamba-ST: State Space Model for Efficient Style Transfer | The goal of style transfer is, given a content image and a style source, generating a new image preserving the content but with the artistic representation of the style source. Most of the state-of-the-art architectures use transformers or diffusion-based models to perform this task, despite the heavy computational bur... | [
"Filippo Botti",
"Alex Ergasti",
"Leonardo Rossi",
"Tomaso Fontanini",
"Claudio Ferrari",
"Massimo Bertozzi",
"Andrea Prati"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-09-16T00:00:00 | https://arxiv.org/abs/2409.10385 | https://arxiv.org/pdf/2409.10385v1 | 2409.10385 | 10.1109/WACV61041.2025.00757 | 15 | 1 | true | https://github.com/FilippoBotti/MambaST | IEEE Workshop/Winter Conference on Applications of Computer Vision | 0.301 |
74f5d524a1422429e46920f2f0727a810a4ba39d5ac04d14bf1a0b74b2240b62 | [
"arxiv",
"semantic_scholar"
] | Mamba for Scalable and Efficient Personalized Recommendations | In this effort, we propose using the Mamba for handling tabular data in personalized recommendation systems. We present the \textit{FT-Mamba} (Feature Tokenizer\,$+$\,Mamba), a novel hybrid model that replaces Transformer layers with Mamba layers within the FT-Transformer architecture, for handling tabular data in pers... | [
"Andrew Starnes",
"Clayton Webster"
] | [
"cs.IR",
"cs.LG"
] | [
"Computer Science"
] | 2024-09-11T00:00:00 | https://arxiv.org/abs/2409.17165 | https://arxiv.org/pdf/2409.17165v1 | 2409.17165 | 10.1109/ICDMW65004.2024.00018 | 1 | 0 | false | null | null | 0.0753 |
85ce6199b28efade71df75c9daa995c6de1d29a0a017d6218ddbefa745a80ad1 | [
"arxiv",
"semantic_scholar"
] | Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models | Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action trajectories. However, diffusion models typically rely on large parameter UNet backbones as policy networks, which can be challenging to deploy o... | [
"Jiahang Cao",
"Qiang Zhang",
"Jingkai Sun",
"Jiaxu Wang",
"Hao Cheng",
"Yulin Li",
"Jun Ma",
"Kun Wu",
"Zhiyuan Xu",
"Yecheng Shao",
"Wen Zhao",
"Gang Han",
"Yijie Guo",
"Renjing Xu"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-09-11T00:00:00 | https://arxiv.org/abs/2409.07163 | https://arxiv.org/pdf/2409.07163v2 | 2409.07163 | 10.1109/IROS60139.2025.11247625 | 27 | 2 | true | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.3618 |
3f271b2ec373e6ebfeb8acf545a539b6e6696563e4e3249d086fcdf90089c12a | [
"arxiv",
"semantic_scholar"
] | Rethinking Mamba in Speech Processing by Self-Supervised Models | The Mamba-based model has demonstrated outstanding performance across tasks in computer vision, natural language processing, and speech processing. However, in the realm of speech processing, the Mamba-based model's performance varies across different tasks. For instance, in tasks such as speech enhancement and spectru... | [
"Xiangyu Zhang",
"Jianbo Ma",
"Mostafa Shahin",
"Beena Ahmed",
"Julien Epps"
] | [
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-09-11T00:00:00 | https://arxiv.org/abs/2409.07273 | https://arxiv.org/pdf/2409.07273v1 | 2409.07273 | 10.1109/ICASSP49660.2025.10889111 | 21 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.3356 |
2bcb9bbb4676e4729f63e30e243485b7fe29ee7706203ae17963de023b63e9d3 | [
"arxiv",
"semantic_scholar"
] | PPMamba: A Pyramid Pooling Local Auxiliary SSM-Based Model for Remote Sensing Image Semantic Segmentation | Semantic segmentation is a vital task in the field of remote sensing (RS). However, conventional convolutional neural network (CNN) and transformer-based models face limitations in capturing long-range dependencies or are often computationally intensive. Recently, an advanced state space model (SSM), namely Mamba, was ... | [
"Yin Hu",
"Xianping Ma",
"Jialu Sui",
"Man-On Pun"
] | [
"cs.CV",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2024-09-10T00:00:00 | https://arxiv.org/abs/2409.06309 | https://arxiv.org/pdf/2409.06309v1 | 2409.06309 | 10.48550/arXiv.2409.06309 | 15 | 1 | false | null | APSIPA Transactions on Signal and Information Processing | 0.301 |
b49024daf167822ad24527ac3f0e0d001039ad4081031d2c4c89012c4d1c4881 | [
"arxiv",
"semantic_scholar"
] | Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model | Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically 200 spanning degrees. Accurate segmentation of vessels in UWF-SLO images is essential for detecting and diagnosing fundus disease. Recent studies have revealed that the selective State Space Model ... | [
"Hongqiu Wang",
"Yixian Chen",
"Wu Chen",
"Huihui Xu",
"Haoyu Zhao",
"Bin Sheng",
"Huazhu Fu",
"Guang Yang",
"Lei Zhu"
] | [
"cs.CV"
] | [
"Computer Science",
"Medicine"
] | 2024-09-06T00:00:00 | https://arxiv.org/abs/2409.04356 | https://arxiv.org/pdf/2409.04356v2 | 2409.04356 | 10.1109/TMI.2025.3584468 | 39 | 2 | false | null | IEEE Transactions on Medical Imaging | 0.4005 |
9b4f3bbe688dbd1f566fa8e12ac5c7f127b62178f155ee201ff5752aa8e3e43e | [
"arxiv",
"semantic_scholar"
] | UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing Images | Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary d... | [
"Lulin Li",
"Ben Chen",
"Xuechao Zou",
"Junliang Xing",
"Pin Tao"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-09-05T00:00:00 | https://arxiv.org/abs/2409.03431 | https://arxiv.org/pdf/2409.03431v3 | 2409.03431 | 10.1109/ICASSP49660.2025.10888896 | 10 | 1 | true | https://github.com/Devin-Egber/UV-Mamba | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.2603 |
acb8b2f2e7cd4538952389ee28530e09fd0eaf51fc77252a267063f4b0070bc8 | [
"arxiv",
"semantic_scholar"
] | Why mamba is effective? Exploit Linear Transformer-Mamba Network for Multi-Modality Image Fusion | Multi-modality image fusion aims to integrate the merits of images from different sources and render high-quality fusion images. However, existing feature extraction and fusion methods are either constrained by inherent local reduction bias and static parameters during inference (CNN) or limited by quadratic computatio... | [
"Chenguang Zhu",
"Shan Gao",
"Huafeng Chen",
"Guangqian Guo",
"Chaowei Wang",
"Yaoxing Wang",
"Chen Shu Lei",
"Quanjiang Fan"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-09-05T00:00:00 | https://arxiv.org/abs/2409.03223 | https://arxiv.org/pdf/2409.03223v1 | 2409.03223 | 10.48550/arXiv.2409.03223 | 3 | 1 | false | null | arXiv.org | 0.1505 |
549036c00508e3797458ae77218a15a49413fd7addbc4be0ade5e2283fe695f0 | [
"arxiv",
"semantic_scholar"
] | Mamba as a motion encoder for robotic imitation learning | Recent advancements in imitation learning, particularly with the integration of LLM techniques, are set to significantly improve robots' dexterity and adaptability. This paper proposes using Mamba, a state-of-the-art architecture with potential applications in LLMs, for robotic imitation learning, highlighting its abil... | [
"Toshiaki Tsuji"
] | [
"cs.RO",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2024-09-04T00:00:00 | https://arxiv.org/abs/2409.02636 | https://arxiv.org/pdf/2409.02636v2 | 2409.02636 | 10.1109/ACCESS.2025.3561283 | 14 | 0 | false | null | IEEE Access | 0.294 |
18e4159ab30bf8b514289ba7eb3f377bf0ebad8bcf8b6a428d7e518a005a3c4f | [
"arxiv",
"semantic_scholar"
] | Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion | Multi-modal image fusion integrates complementary information from different modalities to produce enhanced and informative images. Although State-Space Models, such as Mamba, are proficient in long-range modeling with linear complexity, most Mamba-based approaches use fixed scanning strategies, which can introduce bia... | [
"Ke Cao",
"Xuanhua He",
"Tao Hu",
"Chengjun Xie",
"Man Zhou",
"Jie Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-09-03T00:00:00 | https://arxiv.org/abs/2409.01728 | https://arxiv.org/pdf/2409.01728v2 | 2409.01728 | 10.48550/arXiv.2409.01728 | 11 | 0 | true | https://github.com/caoke-963/Shuffle-Mamba | null | 0.2698 |
a4bf5e6b20be096be6d110f609d6c145202206e16a0c5484b52943666a76727d | [
"arxiv",
"semantic_scholar"
] | Sparse Mamba: Introducing Controllability, Observability, And Stability To Structural State Space Models | Structured state space models' (SSMs) development in recent studies, such as Mamba and Mamba2, outperformed and solved the computational inefficiency of transformers and large language models at small to medium scale. In this work, we introduce the concept of controllability and observability to the original Mamba SSM'... | [
"Emadeldeen Hamdan",
"Hongyi Pan",
"Ahmet Enis Cetin"
] | [
"cs.LG",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2024-08-31T00:00:00 | https://arxiv.org/abs/2409.00563 | https://arxiv.org/pdf/2409.00563v3 | 2409.00563 | null | 2 | 1 | false | null | null | 0.1505 |
10d743176b3caf19bc8a4ba79321dfce17f3d13f6dc3214c8844646c3230e069 | [
"arxiv",
"semantic_scholar"
] | DrowzEE-G-Mamba: Leveraging EEG and State Space Models for Driver Drowsiness Detection | Driver drowsiness is identified as a critical factor in road accidents, necessitating robust detection systems to enhance road safety. This study proposes a driver drowsiness detection system, DrowzEE-G-Mamba, that combines Electroencephalography (EEG) with State Space Models (SSMs). EEG data, known for its sensitivity... | [
"Gourav Siddhad",
"Sayantan Dey",
"Partha Pratim Roy"
] | [
"cs.HC"
] | [
"Computer Science"
] | 2024-08-28T00:00:00 | https://arxiv.org/abs/2408.16145 | https://arxiv.org/pdf/2408.16145v2 | 2408.16145 | 10.1007/978-3-031-78398-2_19 | 14 | 0 | false | null | International Conference on Pattern Recognition | 0.294 |
a29b63ac083c472e85fc6558020bb27a52751eb03841f0e1f70d97e041904732 | [
"arxiv",
"semantic_scholar"
] | The Mamba in the Llama: Distilling and Accelerating Hybrid Models | Linear RNN architectures, like Mamba, can be competitive with Transformer models in language modeling while having advantageous deployment characteristics. Given the focus on training large-scale Transformer models, we consider the challenge of converting these pretrained models for deployment. We demonstrate that it i... | [
"Junxiong Wang",
"Daniele Paliotta",
"Avner May",
"Alexander M. Rush",
"Tri Dao"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-27T00:00:00 | https://arxiv.org/abs/2408.15237 | https://arxiv.org/pdf/2408.15237v4 | 2408.15237 | 10.48550/arXiv.2408.15237 | 124 | 21 | true | https://github.com/jxiw/MambaInLlama | Neural Information Processing Systems | 0.6712 |
adc820d17e0286c20712b5da85bd67091448d32a38ee6b1ba435004083afcc95 | [
"arxiv",
"semantic_scholar"
] | LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation | Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer Vision tasks, including medical image segmentation (MIS). Vision ... | [
"Trung Dinh Quoc Dang",
"Huy Hoang Nguyen",
"Aleksei Tiulpin"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-08-26T00:00:00 | https://arxiv.org/abs/2408.14415 | https://arxiv.org/pdf/2408.14415v1 | 2408.14415 | 10.48550/arXiv.2408.14415 | 33 | 1 | true | https://github.com/Oulu-IMEDS/LoG-VMamba} | arXiv.org | 0.3829 |
602e769094ded2ff52d3b280b2fa6260469d798d5d317ef5081d6da23d07e020 | [
"arxiv",
"semantic_scholar"
] | MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation | State Space Models (SSMs), especially Mamba, have shown great promise in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, accurate medical image segmentation requires the effective learning of both multi-scale detailed feature representation... | [
"Chaowei Chen",
"Li Yu",
"Shiquan Min",
"Shunfang Wang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-25T00:00:00 | https://arxiv.org/abs/2408.13735 | https://arxiv.org/pdf/2408.13735v1 | 2408.13735 | 10.1109/BIBM62325.2024.10821761 | 24 | 3 | false | null | IEEE International Conference on Bioinformatics and Biomedicine | 0.3495 |
0f366417dbf3e5a86a3df3bac529462e05cc1317282e4a45800acd77fc151500 | [
"arxiv",
"semantic_scholar"
] | O-Mamba: O-shape State-Space Model for Underwater Image Enhancement | Underwater image enhancement (UIE) face significant challenges due to complex underwater lighting conditions. Recently, mamba-based methods have achieved promising results in image enhancement tasks. However, these methods commonly rely on Vmamba, which focuses only on spatial information modeling and struggles to deal... | [
"Chenyu Dong",
"Chen Zhao",
"Weiling Cai",
"Bo Yang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-23T00:00:00 | https://arxiv.org/abs/2408.12816 | https://arxiv.org/pdf/2408.12816v1 | 2408.12816 | 10.48550/arXiv.2408.12816 | 14 | 0 | true | https://github.com/chenydong/O-Mamba | arXiv.org | 0.294 |
c425a384a056eb2f91ae813d9b7e534dba5951c3b2d5999ad703361ef85ea290 | [
"arxiv",
"semantic_scholar"
] | Scalable Autoregressive Image Generation with Mamba | We introduce AiM, an autoregressive (AR) image generative model based on Mamba architecture. AiM employs Mamba, a novel state-space model characterized by its exceptional performance for long-sequence modeling with linear time complexity, to supplant the commonly utilized Transformers in AR image generation models, aim... | [
"Haopeng Li",
"Jinyue Yang",
"Kexin Wang",
"Xuerui Qiu",
"Yuhong Chou",
"Xin Li",
"Guoqi Li"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-22T00:00:00 | https://arxiv.org/abs/2408.12245 | https://arxiv.org/pdf/2408.12245v5 | 2408.12245 | 10.48550/arXiv.2408.12245 | 27 | 2 | true | https://github.com/hp-l33/AiM | arXiv.org | 0.3618 |
62ade412ec8f34b88de91430272052390b17064e06a9967400b3b7ef26b6a394 | [
"arxiv",
"semantic_scholar"
] | HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation | In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computatio... | [
"Mingya Zhang",
"Zhihao Chen",
"Yiyuan Ge",
"Xianping Tao"
] | [
"eess.IV",
"cs.CV"
] | [
"Engineering",
"Computer Science"
] | 2024-08-21T00:00:00 | https://arxiv.org/abs/2408.11289 | https://arxiv.org/pdf/2408.11289v2 | 2408.11289 | 10.48550/arXiv.2408.11289 | 15 | 1 | true | https://github.com/simzhangbest/HMT-Unet | arXiv.org | 0.301 |
75369da5543a52c5653255e6411e10663099fd56c21336a8bb37174a2f0d33aa | [
"arxiv",
"semantic_scholar"
] | Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models | Transformer architectures have become a dominant paradigm for domains like language modeling but suffer in many inference settings due to their quadratic-time self-attention. Recently proposed subquadratic architectures, such as Mamba, have shown promise, but have been pretrained with substantially less computational r... | [
"Aviv Bick",
"Kevin Y. Li",
"Eric P. Xing",
"J. Zico Kolter",
"Albert Gu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-19T00:00:00 | https://arxiv.org/abs/2408.10189 | https://arxiv.org/pdf/2408.10189v2 | 2408.10189 | 10.48550/arXiv.2408.10189 | 65 | 10 | true | null | Neural Information Processing Systems | 0.5207 |
f03a3edea781063bc5db3c3639d58518bd5a583364910fc68298674f485032de | [
"arxiv",
"semantic_scholar"
] | MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation | Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range depende... | [
"Fenghe Tang",
"Bingkun Nian",
"Yingtai Li",
"Zihang Jiang",
"Jie Yang",
"Wei Liu",
"S. Kevin Zhou"
] | [
"cs.CV"
] | [
"Computer Science",
"Medicine"
] | 2024-08-15T00:00:00 | https://arxiv.org/abs/2408.08070 | https://arxiv.org/pdf/2408.08070v2 | 2408.08070 | 10.1016/j.media.2025.103606 | 14 | 1 | true | https://github.com/FengheTan9/MambaMIM | Medical Image Analysis, Volume 103, 2025, Article 103606 | 0.294 |
b1e3bffdabb306204280e5039082cd28d3f21b5f6c2a517576d26eaabb0f2c91 | [
"arxiv",
"semantic_scholar"
] | Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval | In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relations. It is important for DR models to balance both efficiency and effectiveness. Pre-trained language models (PLMs), especially Transformer... | [
"Hanqi Zhang",
"Chong Chen",
"Lang Mei",
"Qi Liu",
"Jiaxin Mao"
] | [
"cs.IR"
] | [
"Computer Science"
] | 2024-08-15T00:00:00 | https://arxiv.org/abs/2408.08066 | https://arxiv.org/pdf/2408.08066v2 | 2408.08066 | 10.1145/3627673.3679959 | 11 | 0 | false | null | International Conference on Information and Knowledge Management | 0.2698 |
1b7f5dfe7b8064d871d8720e7064a11411b4216653109d19fc7843f39365b818 | [
"arxiv",
"semantic_scholar"
] | DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs | Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the success of recent state space models in efficiently capturing long-term dependencies, we propose DyG-Mamba by translating dynamic graph modelin... | [
"Dongyuan Li",
"Shiyin Tan",
"Ying Zhang",
"Ming Jin",
"Shirui Pan",
"Manabu Okumura",
"Renhe Jiang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-08-13T00:00:00 | https://arxiv.org/abs/2408.06966 | https://arxiv.org/pdf/2408.06966v2 | 2408.06966 | 10.48550/arXiv.2408.06966 | 25 | 0 | true | https://github.com/Clearloveyuan/DyG-Mamba | arXiv.org | 0.3537 |
ff93bfd81f0e05559b0b8ad246a52003ebe2ae170bdbf715e96c8336c72d3f93 | [
"arxiv",
"semantic_scholar"
] | SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation | In the Sound Event Localization and Detection (SELD) task, Transformer-based models have demonstrated impressive capabilities. However, the quadratic complexity of the Transformer's self-attention mechanism results in computational inefficiencies. In this paper, we propose a network architecture for SELD called SELD-Ma... | [
"Da Mu",
"Zhicheng Zhang",
"Haobo Yue",
"Zehao Wang",
"Jin Tang",
"Jianqin Yin"
] | [
"cs.SD",
"cs.AI",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-08-09T00:00:00 | https://arxiv.org/abs/2408.05057 | https://arxiv.org/pdf/2408.05057v1 | 2408.05057 | 10.48550/arXiv.2408.05057 | 14 | 0 | false | null | arXiv.org | 0.294 |
79a6dac361df2b802ca677d8eda7bdfa71da183ee3464cf5b63da91bc82f1683 | [
"arxiv",
"semantic_scholar"
] | PoseMamba: Monocular 3D Human Pose Estimation with Bidirectional Global-Local Spatio-Temporal State Space Model | Transformers have significantly advanced the field of 3D human pose estimation (HPE). However, existing transformer-based methods primarily use self-attention mechanisms for spatio-temporal modeling, leading to a quadratic complexity, unidirectional modeling of spatio-temporal relationships, and insufficient learning o... | [
"Yunlong Huang",
"Junshuo Liu",
"Ke Xian",
"Robert Caiming Qiu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-07T00:00:00 | https://arxiv.org/abs/2408.03540 | https://arxiv.org/pdf/2408.03540v2 | 2408.03540 | 10.48550/arXiv.2408.03540 | 27 | 5 | false | null | arXiv.org | 0.3891 |
203cb6f2cf76d7696c6adc5d0b7cc3e815c3f918ec8c3993ca3e6ef7ad2bc95b | [
"arxiv",
"semantic_scholar"
] | PackMamba: Efficient Processing of Variable-Length Sequences in Mamba training | With the evolution of large language models, traditional Transformer models become computationally demanding for lengthy sequences due to the quadratic growth in computation with respect to the sequence length. Mamba, emerging as a groundbreaking architecture in the field of generative AI, demonstrates remarkable profi... | [
"Haoran Xu",
"Ziqian Liu",
"Rong Fu",
"Zhongling Su",
"Zerui Wang",
"Zheng Cai",
"Zhilin Pei",
"Xingcheng Zhang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-08-07T00:00:00 | https://arxiv.org/abs/2408.03865 | https://arxiv.org/pdf/2408.03865v2 | 2408.03865 | 10.48550/arXiv.2408.03865 | 2 | 0 | false | null | null | 0.1193 |
6ae585e7734790016a0493e26c934189746a29af04320494f6c2595f7c05dc0b | [
"arxiv",
"semantic_scholar"
] | LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba | Recent Transformer-based diffusion models have shown remarkable performance, largely attributed to the ability of the self-attention mechanism to accurately capture both global and local contexts by computing all-pair interactions among input tokens. However, their quadratic complexity poses significant computational c... | [
"Yunxiang Fu",
"Chaoqi Chen",
"Yizhou Yu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-05T00:00:00 | https://arxiv.org/abs/2408.02615 | https://arxiv.org/pdf/2408.02615v3 | 2408.02615 | 10.48550/arXiv.2408.02615 | 6 | 0 | true | https://github.com/yunxiangfu2001/LaMamba-Diff | arXiv.org | 0.2113 |
736be9490b20227695dbb4d72561838b5a20a780be0c5d8b34f7768b6e2e6b58 | [
"arxiv",
"semantic_scholar"
] | Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data Processing | The field of neuromorphic computing has gained significant attention in recent years, aiming to bridge the gap between the efficiency of biological neural networks and the performance of artificial intelligence systems. This paper introduces Mamba-Spike, a novel neuromorphic architecture that integrates a spiking front... | [
"Jiahao Qin",
"Feng Liu"
] | [
"cs.NE",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-04T00:00:00 | https://arxiv.org/abs/2408.11823 | https://arxiv.org/pdf/2408.11823v1 | 2408.11823 | 10.48550/arXiv.2408.11823 | 16 | 0 | true | https://github.com/ECNU-Cross-Innovation-Lab/Mamba-Spike | Computer Graphics International Conference | 0.3076 |
d0b8753b371dd2a53b2b85300b243fd8d20f6c299421178bcced2955eb9ee7a0 | [
"arxiv",
"semantic_scholar"
] | JambaTalk: Speech-Driven 3D Talking Head Generation Based on Hybrid Transformer-Mamba Model | In recent years, the talking head generation has become a focal point for researchers. Considerable effort is being made to refine lip-sync motion, capture expressive facial expressions, generate natural head poses, and achieve high-quality video. However, no single model has yet achieved equivalence across all quantit... | [
"Farzaneh Jafari",
"Stefano Berretti",
"Anup Basu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-03T00:00:00 | https://arxiv.org/abs/2408.01627 | https://arxiv.org/pdf/2408.01627v3 | 2408.01627 | 10.1145/3793196 | 5 | 0 | false | null | ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) | 0.1945 |
2d304f1a313da1604999471aa52da2e9ec8a579c92406f719faeb29eb7ecdc20 | [
"arxiv",
"semantic_scholar"
] | A Survey of Mamba | As one of the most representative DL techniques, Transformer architecture has empowered numerous advanced models, especially the large language models (LLMs) that comprise billions of parameters, becoming a cornerstone in deep learning. Despite the impressive achievements, Transformers still face inherent limitations, ... | [
"Haohao Qu",
"Liangbo Ning",
"Rui An",
"Wenqi Fan",
"Tyler Derr",
"Hui Liu",
"Xin Xu",
"Qing Li"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-02T00:00:00 | https://arxiv.org/abs/2408.01129 | https://arxiv.org/pdf/2408.01129v8 | 2408.01129 | 10.48550/arXiv.2408.01129 | 95 | 6 | false | null | ACM Transactions on Intelligent Systems and Technology | 0.4956 |
e80bb3d396d591efdd5f9e918e59aca12f31ac284ffb92e81d0244ea75cf2db7 | [
"arxiv",
"semantic_scholar"
] | Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement | Ultra-high-definition (UHD) technology has attracted widespread attention due to its exceptional visual quality, but it also poses new challenges for low-light image enhancement (LLIE) techniques. UHD images inherently possess high computational complexity, leading existing UHD LLIE methods to employ high-magnification... | [
"Wenbin Zou",
"Hongxia Gao",
"Weipeng Yang",
"Tongtong Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-02T00:00:00 | https://arxiv.org/abs/2408.01276 | https://arxiv.org/pdf/2408.01276v1 | 2408.01276 | 10.1145/3664647.3681580 | 114 | 11 | true | https://github.com/AlexZou14/Wave-Mamba | ACM Multimedia | 0.5396 |
7564674a3ecf6f978595083ccb072c225008bca7aefdcdb36cbddc3ff6cf648a | [
"arxiv",
"semantic_scholar"
] | Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification | Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionality and sequential data. To address these issues, we propose the SSM wit... | [
"Muhammad Ahmad",
"Muhammad Hassaan Farooq Butt",
"Muhammad Usama",
"Hamad Ahmed Altuwaijri",
"Manuel Mazzara",
"Salvatore Distefano"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-02T00:00:00 | https://arxiv.org/abs/2408.01224 | https://arxiv.org/pdf/2408.01224v3 | 2408.01224 | 10.1080/2150704X.2025.2461330 | 22 | 0 | true | https://github.com/MHassaanButt/MHA\_SS\_Mamba}{GitHub} | Remote Sensing Letters | 0.3404 |
676e3d46981811b3809b85b07aed9edf3a778c911c975054253cb1f158f067a0 | [
"arxiv",
"semantic_scholar"
] | Enhanced Structured State Space Models via Grouped FIR Filtering and Attention Sink Mechanisms | Structured State Space Models (SSMs) have emerged as compelling alternatives to Transformer architectures, offering linear-time complexity and superior performance in various sequence modeling tasks. Despite their advantages, SSMs like the original Mamba-2 face training difficulties due to the sensitivities introduced ... | [
"Tian Meng",
"Yang Tao",
"Wuliang Yin"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-08-01T00:00:00 | https://arxiv.org/abs/2408.00244 | https://arxiv.org/pdf/2408.00244v1 | 2408.00244 | 10.48550/arXiv.2408.00244 | 1 | 0 | false | null | arXiv.org | 0.0753 |
c234bcf4778d9229da5be5d067e3aaf1cc9786b702a6e1df0ee3ac6b77e76914 | [
"arxiv",
"semantic_scholar"
] | ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2 | Multimodal Large Language Models (MLLMs) have attracted much attention for their multifunctionality. However, traditional Transformer architectures incur significant overhead due to their secondary computational complexity. To address this issue, we introduce ML-Mamba, a multimodal language model, which utilizes the la... | [
"Wenjun Huang",
"Jiakai Pan",
"Jiahao Tang",
"Yanyu Ding",
"Yifei Xing",
"Yuhe Wang",
"Zhengzhuo Wang",
"Jianguo Hu"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-07-29T00:00:00 | https://arxiv.org/abs/2407.19832 | https://arxiv.org/pdf/2407.19832v3 | 2407.19832 | 10.48550/arXiv.2407.19832 | 16 | 1 | false | null | arXiv.org | 0.3076 |
aa675b10e831810363e511c7e5e3f5f245505ba30d0657266b7a53802394b6c1 | [
"arxiv",
"semantic_scholar"
] | MaTrRec: Uniting Mamba and Transformer for Sequential Recommendation | Sequential recommendation systems aim to provide personalized recommendations by analyzing dynamic preferences and dependencies within user behavior sequences. Recently, Transformer models can effectively capture user preferences. However, their quadratic computational complexity limits recommendation performance on lo... | [
"Shun Zhang",
"Runsen Zhang",
"Zhirong Yang"
] | [
"cs.IR"
] | [
"Computer Science"
] | 2024-07-27T00:00:00 | https://arxiv.org/abs/2407.19239 | https://arxiv.org/pdf/2407.19239v1 | 2407.19239 | 10.48550/arXiv.2407.19239 | 10 | 1 | true | https://github.com/Unintelligentmumu/MaTrRec | arXiv.org | 0.2603 |
031b863b5c4dc011f8e5f927472a897158425d938a6063c6a601434a67e2f8e8 | [
"arxiv",
"semantic_scholar"
] | EEG-SSM: Leveraging State-Space Model for Dementia Detection | State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shorter intervals for model training and inference. Traditionally, SSMs capture only the temporal dynamics of time series data, omitting the equally critical spectral features.... | [
"Xuan-The Tran",
"Linh Le",
"Quoc Toan Nguyen",
"Thomas Do",
"Chin-Teng Lin"
] | [
"cs.LG",
"cs.AI",
"cs.HC"
] | [
"Computer Science"
] | 2024-07-25T00:00:00 | https://arxiv.org/abs/2407.17801 | https://arxiv.org/pdf/2407.17801v1 | 2407.17801 | 10.48550/arXiv.2407.17801 | 15 | 0 | false | null | arXiv.org | 0.301 |
2dda4c21f8d27b74a8cccb4fdd908f0d8058ca9b4a3bd3c152aff6022083a677 | [
"arxiv",
"semantic_scholar"
] | Extended invariant cones as Nonlinear Normal Modes of inhomogeneous piecewise linear systems | The aim of this paper is to explore the relationship between invariant cones and nonlinear normal modes in piecewise linear mechanical systems. As a key result, we extend the invariant cone concept, originally established for homogeneous piecewise linear systems, to a class of inhomogeneous continuous piecewise linear ... | [
"A. Yassine Karoui",
"Remco I. Leine"
] | [
"math.DS"
] | [
"Mathematics"
] | 2024-07-22T00:00:00 | https://arxiv.org/abs/2407.16096 | https://arxiv.org/pdf/2407.16096v2 | 2407.16096 | 10.1016/j.ijnonlinmec.2025.105072 | 3 | 0 | false | null | International Journal of Non-Linear Mechanics | 0.1505 |
25ae4c419347bd2dd418c2c811459f89fafc5c82eb7c88a134494d972c7623e0 | [
"arxiv",
"semantic_scholar"
] | Mamba meets crack segmentation | Cracks pose safety risks to infrastructure and cannot be overlooked. The prevailing structures in existing crack segmentation networks predominantly consist of CNNs or Transformers. However, CNNs exhibit a deficiency in global modeling capability, hindering the representation to entire crack features. Transformers can ... | [
"Zhili He",
"Yu-Hsing Wang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2024-07-22T00:00:00 | https://arxiv.org/abs/2407.15714 | https://arxiv.org/pdf/2407.15714v1 | 2407.15714 | 10.48550/arXiv.2407.15714 | 6 | 1 | false | null | arXiv.org | 0.2113 |
590f5d0464b49d74299d45cbb7f8fdd1d1efe0df0abb5ca803fff98cfe314c84 | [
"arxiv",
"semantic_scholar"
] | FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting | In multivariate time-series forecasting (MTSF), extracting the temporal correlations of the input sequences is crucial. While popular Transformer-based predictive models can perform well, their quadratic computational complexity results in inefficiency and high overhead. The recently emerged Mamba, a selective state sp... | [
"Shusen Ma",
"Yu Kang",
"Peng Bai",
"Yun-Bo Zhao"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-07-20T00:00:00 | https://arxiv.org/abs/2407.14814 | https://arxiv.org/pdf/2407.14814v1 | 2407.14814 | 10.48550/arXiv.2407.14814 | 11 | 0 | false | null | arXiv.org | 0.2698 |
0d9bf0f063c75b8012bae8df02c2667ed40b4e7f3a957846b04ab218acbe0e8c | [
"arxiv",
"semantic_scholar"
] | Longhorn: State Space Models are Amortized Online Learners | Modern large language models are built on sequence modeling via next-token prediction. While the Transformer remains the dominant architecture for sequence modeling, its quadratic decoding complexity in sequence length poses a major limitation. State-space models (SSMs) present a competitive alternative, offering linea... | [
"Bo Liu",
"Rui Wang",
"Lemeng Wu",
"Yihao Feng",
"Peter Stone",
"Qiang Liu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-07-19T00:00:00 | https://arxiv.org/abs/2407.14207 | https://arxiv.org/pdf/2407.14207v5 | 2407.14207 | 10.48550/arXiv.2407.14207 | 47 | 8 | false | null | International Conference on Learning Representations | 0.4771 |
8340e233dacd0cad9179b29eec157240edb00cf1977df1c5a0b5c6d7ac997e38 | [
"arxiv",
"semantic_scholar"
] | Investigating the Indirect Object Identification circuit in Mamba | How well will current interpretability techniques generalize to future models? A relevant case study is Mamba, a recent recurrent architecture with scaling comparable to Transformers. We adapt pre-Mamba techniques to Mamba and partially reverse-engineer the circuit responsible for the Indirect Object Identification (IO... | [
"Danielle Ensign",
"Adrià Garriga-Alonso"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-07-19T00:00:00 | https://arxiv.org/abs/2407.14008 | https://arxiv.org/pdf/2407.14008v2 | 2407.14008 | 10.48550/arXiv.2407.14008 | 2 | 0 | false | null | arXiv.org | 0.1193 |
ba2c7979838621cd0a994959a0c0a6b91f64838646337ee0c1109ab0878d1df7 | [
"arxiv",
"semantic_scholar"
] | Mamba-PTQ: Outlier Channels in Recurrent Large Language Models | Modern recurrent layers are emerging as a promising path toward edge deployment of foundation models, especially in the context of large language models (LLMs). Compressing the whole input sequence in a finite-dimensional representation enables recurrent layers to model long-range dependencies while maintaining a const... | [
"Alessandro Pierro",
"Steven Abreu"
] | [
"cs.LG",
"cs.AI",
"cs.NE"
] | [
"Computer Science"
] | 2024-07-17T00:00:00 | https://arxiv.org/abs/2407.12397 | https://arxiv.org/pdf/2407.12397v1 | 2407.12397 | 10.48550/arXiv.2407.12397 | 15 | 2 | false | null | arXiv.org | 0.301 |
3248709460d565dd83408190610f16210a696076703e1b6a980e65fbe7d64050 | [
"arxiv",
"semantic_scholar"
] | Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model | Point cloud segmentation is crucial for robotic visual perception and environmental understanding, enabling applications such as robotic navigation and 3D reconstruction. However, handling the sparse and unordered nature of point cloud data presents challenges for efficient and accurate segmentation. Inspired by the Ma... | [
"Tao Wang",
"Wei Wen",
"Jingzhi Zhai",
"Kang Xu",
"Haoming Luo"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-07-17T00:00:00 | https://arxiv.org/abs/2407.12319 | https://arxiv.org/pdf/2407.12319v1 | 2407.12319 | 10.48550/arXiv.2407.12319 | 11 | 1 | false | null | arXiv.org | 0.2698 |
2a086a78a6dd4877f78c132a2ec4d47d113d8629ea017e3eb629dd254a143dd8 | [
"arxiv",
"semantic_scholar"
] | SR-Mamba: Effective Surgical Phase Recognition with State Space Model | Surgical phase recognition is crucial for enhancing the efficiency and safety of computer-assisted interventions. One of the fundamental challenges involves modeling the long-distance temporal relationships present in surgical videos. Inspired by the recent success of Mamba, a state space model with linear scalability ... | [
"Rui Cao",
"Jiangliu Wang",
"Yun-Hui Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-07-11T00:00:00 | https://arxiv.org/abs/2407.08333 | https://arxiv.org/pdf/2407.08333v1 | 2407.08333 | 10.48550/arXiv.2407.08333 | 5 | 2 | true | https://github.com/rcao-hk/SR-Mamba | arXiv.org | 0.2386 |
32230a00ce8145a9333b78b84431b039b698d0d8e7b2ffe7efa1cb9af0a0b831 | [
"arxiv",
"semantic_scholar"
] | Parallelizing Autoregressive Generation with Variational State Space Models | Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although both enable parallel training, none enable parallel generation due to their autoregressiveness. We propose the variational SSM (VSSM), a varia... | [
"Gaspard Lambrechts",
"Yann Claes",
"Pierre Geurts",
"Damien Ernst"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-07-11T00:00:00 | https://arxiv.org/abs/2407.08415 | https://arxiv.org/pdf/2407.08415v1 | 2407.08415 | 10.48550/arXiv.2407.08415 | 3 | 0 | false | null | arXiv.org | 0.1505 |
195a77399d00621b887fa108ee2e2fefadd831043f4f0d8440e692446bf56d6e | [
"arxiv",
"semantic_scholar"
] | HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model | Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, HTD faces challenges due to limited prior knowledge and spectral variation, leading to underfitting models and unreliable performance. To address these cha... | [
"Dunbin Shen",
"Xuanbing Zhu",
"Jiacheng Tian",
"Jianjun Liu",
"Zhenrong Du",
"Hongyu Wang",
"Xiaorui Ma"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-07-09T00:00:00 | https://arxiv.org/abs/2407.06841 | https://arxiv.org/pdf/2407.06841v2 | 2407.06841 | 10.1109/TGRS.2025.3547019 | 10 | 0 | true | https://github.com/shendb2022/HTD-Mamba} | IEEE Transactions on Geoscience and Remote Sensing | 0.2603 |
1b49533495e4387a8ee171a6eba1ea84643ffa527d4de5c9dd0c22bbc23bb186 | [
"arxiv",
"semantic_scholar"
] | Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples while preserving knowledge of previously learned classes. Existing methods face a critical dilemma: static architectures rely on a fixed parameter space to learn from data that arrive sequentially, prone to overf... | [
"Xiaojie Li",
"Yibo Yang",
"Jianlong Wu",
"Yue Yu",
"Ming-Hsuan Yang",
"Liqiang Nie",
"Min Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-07-08T00:00:00 | https://arxiv.org/abs/2407.06136 | https://arxiv.org/pdf/2407.06136v3 | 2407.06136 | 10.48550/arXiv.2407.06136 | 14 | 0 | true | https://github.com/xiaojieli0903/Mamba-FSCIL | arXiv.org | 0.294 |
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