Watch Video, Catch Keyword: Context-aware Keyword Attention for Moment Retrieval and Highlight Detection (2501.02504v1)

Abstract: The goal of video moment retrieval and highlight detection is to identify specific segments and highlights based on a given text query. With the rapid growth of video content and the overlap between these tasks, recent works have addressed both simultaneously. However, they still struggle to fully capture the overall video context, making it challenging to determine which words are most relevant. In this paper, we present a novel Video Context-aware Keyword Attention module that overcomes this limitation by capturing keyword variation within the context of the entire video. To achieve this, we introduce a video context clustering module that provides concise representations of the overall video context, thereby enhancing the understanding of keyword dynamics. Furthermore, we propose a keyword weight detection module with keyword-aware contrastive learning that incorporates keyword information to enhance fine-grained alignment between visual and textual features. Extensive experiments on the QVHighlights, TVSum, and Charades-STA benchmarks demonstrate that our proposed method significantly improves performance in moment retrieval and highlight detection tasks compared to existing approaches. Our code is available at: https://github.com/VisualAIKHU/Keyword-DETR

• The paper introduces a novel Video Context-aware Keyword Attention module that dynamically captures keyword importance across video contexts.
• It employs temporally-weighted video clustering and keyword-aware contrastive loss to enhance text-video feature alignment and retrieval accuracy.
• Experiments on QVHighlights, TVSum, and Charades-STA datasets demonstrate significant performance improvements in moment retrieval and highlight detection.

Overview of "Watch Video, Catch Keyword: Context-aware Keyword Attention for Moment Retrieval and Highlight Detection"

The paper "Watch Video, Catch Keyword: Context-aware Keyword Attention for Moment Retrieval and Highlight Detection" addresses a nuanced challenge in video processing: the simultaneous execution of video moment retrieval and highlight detection. These tasks are essential for extracting relevant video segments based on text queries—a necessity driven by the rapid proliferation of video content. The primary contribution is the introduction of a novel Video Context-aware Keyword Attention module that outperforms existing approaches by effectively capturing keyword variation within the entire context of a video.

Technical Contributions

The paper's central advancement is a mechanism to capture keyword dynamics—the variance in keyword importance across different segments of a video—which previous models typically overlooked. The proposed system consists of two interrelated novel components:

1. Video Context-aware Keyword Attention Module: This module is pivotal in capturing the overall context of a video and identifying the contextual importance of keywords within text queries.
   • Video Context Clustering: This employs temporally-weighted clustering to encapsulate video context by grouping perceptually similar scenes, thereby providing a concise video representation that aids in understanding keyword variation.
   • Keyword Weight Detection: The module calculates the importance of keywords by assessing their semantic alignment with video clusters, utilizing a keyword-aware contrastive learning approach to enhance text-video feature alignment.
2. Keyword-aware Contrastive Loss: This element enhances text-video feature alignment by focusing on the dynamic importance of keywords, thereby refining both intra-video and inter-video representations to achieve more accurate moment retrieval and highlight detection.

Results and Claims

The authors substantiate their claims with empirical evidence derived from extensive experiments conducted on well-regarded benchmarks such as QVHighlights, TVSum, and Charades-STA. The results indicate significant improvements in performance metrics for moment retrieval and highlight detection tasks, setting new standards in these areas:

• On the QVHighlights dataset, the proposed approach achieved notable performance gains, with [email protected] and mean Average Precision metrics surpassing those of recent methods.
• The framework also outperformed existing approaches on the TVSum and Charades-STA datasets, demonstrating robustness and effectiveness across diverse test conditions.

Implications and Future Developments

The implications of this research are manifold, both theoretically and practically. Theoretically, it advances our understanding of multimodal learning by integrating contextual video analysis with keyword dynamics, thus contributing to the broader research area of video-text interaction. Practically, the system's ability to refine video moment retrieval and highlight detection can significantly benefit applications in video editing, archiving, and content recommendation systems.

Looking forward, potential expansions of this work could involve exploring more sophisticated methods for audio-visual integration to complement the current focus on visual content. Additionally, adapting the system for real-time applications could make it highly valuable for live-streaming services aiming to enhance user engagement through automated content summarization.

In summary, this paper represents a significant step forward in the integration of text and video analysis, providing a more nuanced understanding of moment retrieval and highlight detection through the innovative application of context-aware techniques. Its contributions pave the way for more sophisticated and user-centered multimedia content interaction systems.