Vision-language models (VLMs) can answer questions about hour-long videos, but processing every frame is prohibitively expensive, even though the evidence for a question usually spans only a few seconds. Video agents, i.e., harness programs wrapped around a frozen VLM, address this by observing the video selectively, y...
Susan Liang, Jian-Min Wu, Da-Xiang Dong· 0 citations
This visually grounded, multi-probe evaluation first verifies that a model recognizes each concept from the image, then tests its recoverability through multiple-choice, short-answer, and indirect queries.
Zhang-Yun Tan, Ze-Liang Zhang, Jia-Ni Liu et al.· 1 citation
A benchmark that tests the ability of agentic video understanding by asking agents to reconstruct real-world videos as animated Blender scenes, and shows that programmatic reconstruction is a viable test of agentic video understanding, and that semantic retention remains the main challenge.
Y. Y. Tang, D. Shimada, Jia-Yue Meng et al.· 0 citations
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