arXiv Computer Vision By Yihao Hu, Pan Wang, Yitong Lin, Zhihao Wen, Deyuan Liu, Hang Wang, Mingda Wan, Yiwen Zhao, Yutong Li, Yuchen Mao, Tao Lin

Supervising the Visible State: Separating Representation from View Selection in Streaming Video

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arXiv Computer Vision
4d ago

TwinPast: Benchmarking Historical Evidence Use in Streaming Video Understanding with Evidence Contrastive Video Pairs

TwinPast is a new benchmark that tests whether streaming video models use historical evidence by presenting pairs of videos that differ only in critical past events while keeping recent context, questions, and options the same. The study evaluates 34 model configurations on 350 human‑reviewed pairs across five historical tasks, finding that most models rely on recent frames or the question alone, with only a few showing improved performance when historical inputs are provided. Even the best streaming system scores only 49.14% PairAcc, far below general‑purpose models, and case studies reveal that models often select states from the wrong time or contradict the recorded event order.

By Haotian Yang, Yong Chen, Cheng Peng, Haobo Wang, Zhongle Xie, Xuanru Li, Gang Chen
arXiv Computer Vision
Sep 2

StreamScout: Learning When to Look Deeper for Streaming Video Understanding

arXiv:2609.00291v1 Announce Type: new Abstract: Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily...

By Ce Zhang, Jing Bi, Jinxi He, Jianshu Zhang, Jingyang Lin, Yunzhong Xiao, Minghao Fu, Yaqi Xie, Zhentao Xie, Weicong Chen, Katia Sycara, Ming Zhou
arXiv Computation and Language
Sep 28

TRACE: Temporal Audit and Condition-aware Evaluation of Streaming Video Understanding

TRACE (Temporal Audit and Condition-aware Evaluation) is a new benchmark and evaluation framework for streaming video understanding that explicitly records when evidence becomes valid, how visual history is maintained, and how responses are triggered. It combines temporally audited visual tasks, evidence timing, instruction-dependent trigger annotations, a unified causal Core–Adapter protocol, and multidimensional reporting of answer quality, timeliness, response-selection behavior, workload, completion, and reliability. Using 1,240 records from 517 videos, TRACE evaluated eight publicly available models in eight configurations, revealing that similar QA accuracy can hide significant differences in completion, answer validity, generation workload, and proactive performance metrics such as response delay, false alarms, and missed target windows.

By Yibo Ma, Qianqian Zhang, Peng Liu, Tiancheng Zhao