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.
Abstract (translated)
从未修剪视频中检索部分相关片段仍面临两大持续挑战：文本与视频片段间的信息密度不匹配，以及有限的注意力机制会忽略语义焦点与事件关联。我们提出KDC-Net（知识精炼的双视角上下文感知网络），从文本与视觉双视角应对这些问题。在文本侧，层次化语义聚合模块捕获并自适应融合多尺度短语线索以丰富查询语义；在视频侧，动态时序注意力机制采用相对位置编码与自适应时序窗口，突出具有局部时序连贯性的关键事件。此外，结合时序连续性感知优化的动态CLIP蒸馏策略，确保了片段感知且目标对齐的知识迁移。在PRVR基准测试中，KDC-Net持续优于现有方法，尤其在低片段-视频比场景下表现突出。
URL
https://arxiv.org/abs/2603.23902
