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Jiaqi Tang PRO

Jiaqi-hkust
7 12 21
mbzx2002's profile picture rtliu's profile picture BrillianTop1's profile picture
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https://jqt.me/

AI & ML interests

Multimodal Large Language Model

Recent Activity

liked a dataset about 2 hours ago
futurex-ai/Futurex-Past
reacted to theirpost with ➕ 2 days ago
IQA-T1: Evidence‑Based Image Quality Assessment with MLLMs Most MLLMs are blind to low‑level degradations—noise, blur, compression artifacts look the same as clean images in their internal representations. That leads to quality scores based on semantic “gut feeling” rather than real perceptual evidence. IQA-T1 changes that. We equip the model with a toolbox of 15 perceptual tools (noise residual maps, Fourier spectra, gradient maps, etc.) that generate structured visual evidence on demand. The model learns how to use tools via supervised fine‑tuning on our Q‑Tool dataset (11k evidence‑grounded reasoning chains), and when to call them via GRPO reinforcement learning that balances accuracy, tool count, and redundancy. The result: SOTA performance across 7 benchmarks (avg PLCC 0.795), using only 2.34 tools per image on average. Every predicted score is now interpretable and backed by hard visual evidence. All code, weights, dataset, and demo are open. Check them out and give it a spin! 📄 arxiv.org/abs/2607.12375v1 💻 github.com/zibuyu-02/IQA-T1 🤗 model/data: huggingface.co/zibuyu-02/IQA-T1 🎮 demo: huggingface.co/spaces/Jiaqi-hkust/IQA-T1
reacted to theirpost with 🤝 2 days ago
IQA-T1: Evidence‑Based Image Quality Assessment with MLLMs Most MLLMs are blind to low‑level degradations—noise, blur, compression artifacts look the same as clean images in their internal representations. That leads to quality scores based on semantic “gut feeling” rather than real perceptual evidence. IQA-T1 changes that. We equip the model with a toolbox of 15 perceptual tools (noise residual maps, Fourier spectra, gradient maps, etc.) that generate structured visual evidence on demand. The model learns how to use tools via supervised fine‑tuning on our Q‑Tool dataset (11k evidence‑grounded reasoning chains), and when to call them via GRPO reinforcement learning that balances accuracy, tool count, and redundancy. The result: SOTA performance across 7 benchmarks (avg PLCC 0.795), using only 2.34 tools per image on average. Every predicted score is now interpretable and backed by hard visual evidence. All code, weights, dataset, and demo are open. Check them out and give it a spin! 📄 arxiv.org/abs/2607.12375v1 💻 github.com/zibuyu-02/IQA-T1 🤗 model/data: huggingface.co/zibuyu-02/IQA-T1 🎮 demo: huggingface.co/spaces/Jiaqi-hkust/IQA-T1
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