arxiv:2606.08063
Jiaqi Tang PRO
Jiaqi-hkust
AI & ML interests
Multimodal Large Language Model
Recent Activity
reacted to theirpost with ➕ about 9 hours 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 🤝 about 10 hours 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 😎 1 day 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-T1Organizations
