arXiv Computer Vision
Dynamic Hub-and-Spoke Memory for Streaming Video Understanding
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arXiv Computer Vision
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MEMO: Multi-Level Entity-Aware Memory for Streaming Video Understanding
arXiv:2609.38900v1 Announce Type: new Abstract: Streaming video understanding requires models to process unbounded visual streams while preserving rich visual semantics across vast temporal horizons,...
arXiv Computer Vision
Oct 2
FlashBack: Knowing When to Remember in Streaming Vision-Language Models
arXiv:2610.01192v1 Announce Type: new Abstract: Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between...
arXiv AI
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What Should a Streaming Video Model Remember?
arXiv:2606. 16353v1 Announce Type: cross Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets.
arXiv AI
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Watch-Think-Interact: Bootstrapping Long-Horizon Multi-Turn Streaming Video Reasoning with Reinforcement Learning
The paper introduces Watch-Think-Interact (WTI), a closed-loop framework for multi-question streaming video reasoning that maintains compact natural-language memory entries linked to video time ranges. WTI decides whether to answer, continue watching, or recall relevant past intervals for each question, avoiding replay of the full history. The authors build a large dataset, WTI-82K, and a training method, Stream-GDPO, achieving state‑of‑the‑art performance on StreamingBench and OVO-Bench.
arXiv AI
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Memory as Weights: Internalizing Long-Term History for Streaming Videos
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arXiv Computation and Language
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MemoryCard: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering
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