Archived
2026-09-30Beyond the Timeline: Augmenting Long-Video Memory with Grounded Entity Biographies
Hui Ren, Lei Fan, Henry Pao, Han Guo, Zeeshan Zia, Ying Chen, et al.
arXiv:2609.38155v1Long videos are hard to search because the same person, object, or place can appear across many clips, but simple timelines often lose track of who is who. This paper introduces Grounded Entity Biographies, a memory system that builds a kind of identity profile for each visually grounded entity. Instead of storing only chronological events, it links observations of the same physical instance across time and keeps the surrounding context attached. Then, when a question comes in, the model can retrieve both the relevant episode and the entity’s biography, letting it follow that object through the video’s history. That matters because many long-video questions depend on continuity of identity, not just event order. On benchmarks spanning hours to days and even weeks, the approach improves both multiple-choice and open-ended question answering, including a strong result on EgoLifeQA. More broadly, it points toward video AI that remembers entities the way people do: by recognizing persistent individuals, not just isolated moments.
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Previous daily papers
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