3 papers
cs.CV2026
CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding
Lihao Zheng, Zhenwei Shao, Yu Zhou +5
Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding, often exhibiting spatial hall…
cs.CV2026
Progressive Video Condensation with MLLM Agent for Long-form Video Understanding
Yufei Yin, Yuchen Xing, Qianke Meng +3
Understanding long videos requires extracting query-relevant information from long sequences under tight compute budgets. Existing text-then-LLM pipelines lose fine-grained visual…
cs.CV2024
Sparrow: Data-Efficient Video-LLM with Text-to-Image Augmentation
Shukang Yin, Chaoyou Fu, Sirui Zhao +7
Recent years have seen the success of Multimodal Large Language Models (MLLMs) in the domain of vision understanding. The success of these models can largely be attributed to the d…