Streaming video understanding requires Video Large Language Models (Video-LLMs) to reason over continuous visual streams under causal constraints. As the visual history grows, a bounded visual?processing budget requires evidence selection that balances temporal recency with query relevance. Recent-only selection exclud...
Xiang Hu, Jia-Zuo Yu, Lu Zhang et al.· 0 citations
Visual object tracking aims to continuously locate specific targets within sequential frames, evolving from single-modal methods to multi-modal ones. However, existing multi-modal trackers are typically designed for fixed modality combinations, requiring separate models for different inputs. This leads to a poor adapta...
Hao Li, Yun-Zhi Zhuge, Wenning Hao et al.· 0 citations
GROVE is introduced, a training-free framework that supports both behaviors with one memory grown causally from a continuous video stream, and achieves the best results among the compared methods.
Sitong Gong, Caixin Kang, Tianyu Yan et al.· 0 citations
This work demonstrates that the aligned LLM with a general-purpose vision encoder can effectively enhance downstream VQA performance with task-specific encoders, and investigates several alignment strategies between the aligned LLM and new task-specific encoders.
Jiazuo Yu, Yunzhi Zhuge, Lu Zhang et al.· International Journal of Com...· 0 citations
ERA establishes logit-preserving visual token pruning as a principled framework for efficient MLLMs, unifying theoretical foundation, algorithmic design, and practical deployment.
Yuhao Wang, Mu Qiao, Haiwen Diao et al.· arXiv.org· 0 citations
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