arXiv:2606. 28326v1 Announce Type: cross Abstract: This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries.
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Most keyframe selection studies focus on offline settings, assuming access to the full video and query in advance. In contrast, real-world streaming scenarios require online frame selection under unkn...
arXiv:2607. 00446v1 Announce Type: cross Abstract: As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video reasoning) and subsequently perform fine-grained, query-conditioned tasks (intra-video reasoning) within the retrieved content, such as temporal grounding.
SVMemAgent introduces a streaming video memory (SVMem) that continuously updates a compact representation of observed frames for online keyframe selection without prior knowledge of video length, query, or future frames. The agent decides at each timestep whether to replace an existing memory frame with a new one or discard it, trained via Group Relative Policy Optimization using task-driven rewards from question-answer pairs. Experiments demonstrate that SVMemAgent outperforms existing online baselines and rivals offline methods, and its learned policy tends to favor frames containing textual information, potentially aiding downstream VideoQA tasks.
arXiv:2608.20805v1 Announce Type: new Abstract: Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Altho...
arXiv:2606. 05275v1 Announce Type: cross Abstract: We study the personal camera roll visual question answering setting.
arXiv:2608. 12627v1 Announce Type: cross Abstract: Long-horizon egocentric memory transforms continuous first-person video and audio into a searchable record of past experiences.