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cs.CV2026
ReQuest: Rethinking-based Question-Aware Frame Selection for Long-Form Video QA
Minkuk Kim, Suyong Yun, Young Tae Kim +3
Recent multimodal large language models (MLLMs) have substantially advanced video understanding, yet long-form video QA remains challenging under fixed input token budgets, where u…
cs.CV2026
EVIDENT: Routing MLLM Adaptation through Entity-Grounded Visual Evidence for Cross-Domain Video Temporal Grounding
Geo Ahn, Jiwook Han, Youngrae Kim +2
Fine-tuning MLLMs for Video Temporal Grounding (VTG) often improves in-domain performance but degrades sharply under domain shift. In this work, we find that this failure is primar…
cs.CV2026
Which Way Did It Move? Diagnosing and Overcoming Directional Motion Blindness in Video-LLMs
Jongseo Lee, Hyuntak Lee, Sunghun Kim +3
Video Large Language Models (Video-LLMs) have made rapid progress on temporal video understanding, yet many fail at a basic perceptual primitive: signed image-plane motion directio…