4 citations · 9 across the 4 of their papers we have counts for
5 papers · 1 filter
CMC-Bench: Towards a New Paradigm of Visual Signal Compression
Chunyi Li, Xiele Wu, Haoning Wu +7
Ultra-low bitrate image compression is a challenging and demanding topic. With the development of Large Multimodal Models (LMMs), a Cross Modality Compression (CMC) paradigm of Ima…
Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
Chunyi Li, Haoning Wu, Zicheng Zhang +7
With the rapid evolution of the Text-to-Image (T2I) model in recent years, their unsatisfactory generation result has become a challenge. However, uniformly refining AI-Generated I…
Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
Haoning Wu, Zicheng Zhang, Erli Zhang +11
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range o…
AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment
Chunyi Li, Zicheng Zhang, Haoning Wu +5
With the rapid advancements of the text-to-image generative model, AI-generated images (AGIs) have been widely applied to entertainment, education, social media, etc. However, cons…
A Perceptual Quality Assessment Exploration for AIGC Images
Zicheng Zhang, Chunyi Li, Wei Sun +3
\underline{AI} \underline{G}enerated \underline{C}ontent (\textbf{AIGC}) has gained widespread attention with the increasing efficiency of deep learning in content creation. AIGC,…