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arXiv · 2603.23902

Knowledge-Refined Dual Context-Aware Network for Partially Relevant Video Retrieval

Junkai Yang·Qirui Wang·Yaoqing Jin·Shuai Ma·Minghan Xu·Shanmin Pang
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Abstract

Retrieving partially relevant segments from untrimmed videos remains difficult due to two persistent challenges: the mismatch in information density between text and video segments, and limited attention mechanisms that overlook semantic focus and event correlations. We present KDC-Net, a Knowledge-Refined Dual Context-Aware Network that tackles these issues from both textual and visual perspectives. On the text side, a Hierarchical Semantic Aggregation module captures and adaptively fuses multi-scale phrase cues to enrich query semantics. On the video side, a Dynamic Temporal Attention mechanism employs relative positional encoding and adaptive temporal windows to highlight key events with local temporal coherence. Additionally, a dynamic CLIP-based distillation strategy, enhanced with temporal-continuity-aware refinement, ensures segment-aware and objective-aligned knowledge transfer. Experiments on PRVR benchmarks show that KDC-Net consistently outperforms state-of-the-art methods, especially under low moment-to-video ratios.

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Junkai Yang, Qirui Wang, Yaoqing Jin, Shuai Ma, Minghan Xu, Shanmin Pang. 2026-03-25. Knowledge-Refined Dual Context-Aware Network for Partially Relevant Video Retrieval. https://arxiv.org/abs/2603.23902

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