Bridging the Semantic Granularity Gap Between Text and Frame Representations for Partially Relevant Video Retrieval

Partially Relevant Video Retrieval (PRVR) addresses the challenges of text-to-video retrieval in real-world scenarios where untrimmed videos are prevalent. Traditional PRVR methods encode videos at two feature scales: (1) frame-level to capture fine details, and (2) clip-level to recognize broader content. However, these approaches align both scales with a single sentence representation, leading to suboptimal performance. In particular, we point out the level mismatch in aligning frame-level video features with a sentence representation, as the entire meaning of a sentence contains broader and more diverse content than what frame-level features can encode. This misalignment causes frame-level features to capture broader contexts and overlook local fine details. To tackle this issue, we propose a framework that represents a sentence as a set of multiple components, where each component aligns with frame-level semantics. Specifically, we introduce Semantic-Decomposed Matching (SDM) to adjust the granularity of the text description to match them with frame-level video features. In addition to the matching process, we develop the Adaptive Local Aggregator (ALA) to enhance video encoding in capturing finer local details, ensuring precise text-video alignment at the frame level. ALA adaptively integrates multi-scale local details within short temporal spans obtained by enforcing a strict temporal aggregation range. Finally, we reinforce detailed encoding at the frame level with newly designed objectives for both modalities. Extensive experiments integrating our framework with existing clip branches demonstrate its effectiveness and applicability, highlighting significant improvements in PRVR performance.