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arXiv Computer Vision By Senbin Xu, Zijun Zhang, Xinhua Wu, Li He, Jubo Feng, Li Qiao, Zhen Gao, Yan Zhang, Jixiang Luo, Rongrong Ji

SPARE: Shared Perception and Adaptive Retrieval of Evidence for Streaming Video Understanding

Read the original on arXiv Computer Vision →

SPARE is a framework for streaming video understanding that adaptively retrieves historical evidence when recent observations are insufficient to answer a question. It uses a lightweight gate to decide when to fetch relevant past clips via a multimodal embedding model, and trains the system with LoRA instruction tuning to jointly use recent and retrieved context while abstaining when evidence is lacking. By sharing a visual encoder between understanding and retrieval branches and aligning features with linear adapters, SPARE achieves state‑of‑the‑art accuracy on OVO‑Bench and StreamingBench, outperforming Qwen3‑VL‑8B‑Instruct by over 20 percentage points on OVO‑Bench and 14 on StreamingBench.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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