2 citations · 4 across the 10 of their papers we have counts for
5 papers · 1 filter
R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?
Chunyi Li, Jianbo Zhang, Zicheng Zhang +8
The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images…
Free-VSC: Free Semantics from Visual Foundation Models for Unsupervised Video Semantic Compression
Yuan Tian, Guo Lu, Guangtao Zhai
Unsupervised video semantic compression (UVSC), i.e., compressing videos to better support various analysis tasks, has recently garnered attention. However, the semantic richness o…
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…
SMC++: Masked Learning of Unsupervised Video Semantic Compression
Yuan Tian, Xiaoyue Ling, Cong Geng +3
Most video compression methods focus on human visual perception, neglecting semantic preservation. This leads to severe semantic loss during the compression, hampering downstream v…
MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model
Chunyi Li, Guo Lu, Donghui Feng +6
With the evolution of storage and communication protocols, ultra-low bitrate image compression has become a highly demanding topic. However, existing compression algorithms must sa…