Video temporal grounding supports applications such as video search, content review, and automated editing by localizing events described in natural language. Yet existing generative models typically output timestamps without explicit interval-level confidence scores to guide candidate selection. We separate candidate...
Jin-Hao Chen, Ben-Lei Cui, Rui-Jian Jia et al.· 0 citations
Recent advances in Multimodal Large Language Models (MLLMs) have significantly improved remote sensing (RS) multimodal understanding. Language-conditioned segmentation is crucial for fine-grained target understanding in Unmanned Aerial Vehicle (UAV) videos. However, this task remains challenging due to the prevalence o...
Kaiwen Jing, Ruijian Jia, Bingyao Li et al.· arXiv.org· 0 citations
Long-form video understanding requires locating sparse, question-relevant evidence in long, multimodal videos. Real-world video distributions differ in modality-specific information density, content structure, and evidence patterns, causing fixed video-agent designs to incur redundant processing or fail when mismatched...
Ben-Lei Cui, Ruize Wang, Junjie Li et al.· 1 citation
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