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20242026
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cs.CV2026

Revisiting Uncertainty: On Evidential Learning for Partially Relevant Video Retrieval

Jun Li, Peifeng Lai, Xuhang Lou +5

Partially relevant video retrieval aims to retrieve untrimmed videos using text queries that describe only partial content. However, the inherent asymmetry between brief queries an…

cs.CV2026

From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents

Niu Lian, Yuting Wang, Hanshu Yao +5

While multimodal large language models have demonstrated impressive short-term reasoning, they struggle with long-horizon video understanding due to limited context windows and sta…

cs.CV2026

Imagine Before Concentration: Diffusion-Guided Registers Enhance Partially Relevant Video Retrieval

Jun Li, Xuhang Lou, Jinpeng Wang +4

Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos based on text queries that describe only partial events. Existing methods suffer from incomplete global…

cs.CV2024

Efficient Self-Supervised Video Hashing with Selective State Spaces

Jinpeng Wang, Niu Lian, Jun Li +5

Self-supervised video hashing (SSVH) is a practical task in video indexing and retrieval. Although Transformers are predominant in SSVH for their impressive temporal modeling capab…

cs.CV2024

GMMFormer v2: An Uncertainty-aware Framework for Partially Relevant Video Retrieval

Yuting Wang, Jinpeng Wang, Bin Chen +3

Given a text query, partially relevant video retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments. Due to the lack of moment annotations, the uncertainty…