Knowledge-Refined Dual Context-Aware Network for Partially Relevant Video Retrieval
Summary: arXiv:2603.23902v1 Announce Type: cross
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.
Introduction
In the realm of video retrieval, particularly when dealing with untrimmed videos, the task of identifying partially relevant segments presents significant challenges. Existing methodologies often struggle with the inherent discrepancies in information density between textual cues and video content. Additionally, many current systems employ limited attention mechanisms, which fail to adequately highlight key semantic elements and the correlations between different events within the video.
KDC-Net Overview
KDC-Net introduces a novel approach that addresses these challenges through a dual context-aware framework, enhancing both textual and visual processing capabilities. The architecture comprises two primary components:
- Hierarchical Semantic Aggregation Module: This innovative module is designed to capture and adaptively fuse multi-scale phrase cues. By enriching query semantics, it significantly improves the accuracy of text-based queries against video content.
- Dynamic Temporal Attention Mechanism: On the video processing side, this mechanism utilizes relative positional encoding and adaptive temporal windows. It effectively highlights key events while maintaining local temporal coherence, ensuring that the most relevant segments are prioritized during retrieval.
Knowledge Transfer and Refinement
To enhance the retrieval process further, KDC-Net incorporates a dynamic CLIP-based distillation strategy. This strategy is augmented with temporal-continuity-aware refinement, which ensures that knowledge transfer is not only segment-aware but also aligns with the objectives of the retrieval task. By refining the knowledge transfer process, KDC-Net enhances the model’s ability to discern and retrieve relevant segments effectively.
Experimental Results
The efficacy of KDC-Net has been rigorously tested against established benchmarks, specifically the PRVR (Partially Relevant Video Retrieval) datasets. Results indicate that KDC-Net consistently outperforms state-of-the-art methodologies, particularly in scenarios characterized by low moment-to-video ratios. This performance is critical, as it demonstrates KDC-Net’s robustness in handling complex retrieval tasks where relevant information is sparse.
Conclusion
In conclusion, KDC-Net represents a significant advancement in the field of partially relevant video retrieval. By addressing the core challenges of information density mismatch and attention limitations, it sets a new standard for video retrieval systems. The integration of hierarchical semantic aggregation and dynamic temporal attention mechanisms, coupled with a sophisticated knowledge transfer strategy, positions KDC-Net as a leading solution for efficient and effective video segment retrieval.
As the demand for advanced video retrieval systems continues to grow, innovations like KDC-Net will play a pivotal role in shaping the future of content accessibility and user experience in multimedia environments.