Memory Internalization via Test-Time Training for Long-Video Understanding
Long-video understanding often requires answering many questions about a single video whose evidence is distributed across extended temporal contexts. Conventional video-language models repeatedly encode frames or retrieve visual evidence for every question. Parameterized video memory offers a write-once alternative, but existing methods rely primarily on synthesized text and fixed adaptation configurations, leaving open where video knowledge should be written and how visual evidence should shape the memory. We introduce Memory Internalization via Test-Time Training (MIT^3), a per-video adaptation framework that writes each long video into compact, video-specific adaptive parameters for v...
| Authors | Haonan Zhao, Jiaxuan Chen, Qingyang Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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