Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos (1906.02497v2)

Abstract: Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one aspect of this emerging task, such as the query representation learning, video context modeling or multi-modal fusion, thus fail to develop a comprehensive system for further performance improvement. In this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to consider multiple crucial factors for this challenging task, including (1) the syntactic structure of natural language queries; (2) long-range semantic dependencies in video context and (3) the sufficient cross-modal interaction. Specifically, we devise a syntactic GCN to leverage the syntactic structure of queries for fine-grained representation learning, propose a multi-head self-attention to capture long-range semantic dependencies from video context, and next employ a multi-stage cross-modal interaction to explore the potential relations of video and query contents. The extensive experiments demonstrate the effectiveness of our proposed method.

• The paper introduces the Cross-Modal Interaction Network that leverages a syntactic GCN and multi-head self-attention to enhance query-based moment retrieval.
• It achieves notable performance improvements, with R@1 scores of 43.40% on ActivityCaption and 24.64% on TACoS.
• Its multi-stage cross-modal interaction effectively fuses video and query features, advancing precision in multimedia search applications.

Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos

The task of Query-Based Moment Retrieval involves identifying specific segments or moments within untrimmed videos that correspond semantically to given natural language queries. This endeavor is crucial for advancing multimedia information retrieval capabilities, as videos often contain a plethora of complex events, only a fraction of which may align with a particular query. The discussed paper, authored by Zhu Zhang et al., introduces a comprehensive model known as the Cross-Modal Interaction Network (CMIN) designed to enhance retrieval performance by addressing multiple pivotal challenges inherent to this task.

Key Contributions

1. Syntactic GCN: The authors propose employing a Syntactic Graph Convolution Network (GCN) to encapsulate the syntactic structure of queries. Unlike methods that merely encode queries using recurrent neural networks (RNNs), the syntactic GCN leverages the inherent grammatical structure of language, fostering a more nuanced and contextually aware query representation.
2. Multi-Head Self-Attention: The work employs a multi-head self-attention mechanism to capture long-range semantic dependencies in video contexts. This approach enhances model understanding by allowing frames to interact not just with immediate neighbors but also with distant frames, addressing a typical limitation of sequential models such as RNNs in handling non-adjacent dependencies.
3. Multi-Stage Cross-Modal Interaction: To synergize video and query contents, the model incorporates a multi-stage interaction strategy. Initial stages leverage attention mechanisms to aggregate crucial query attributes, while subsequent phases employ cross gates and bilinear fusion to refine feature representations. This cross-modal engagement ensures that the retrieval model gains a comprehensive understanding of both modalities.

Numerical Results and Efficacy

The CMIN model's efficacy is evaluated on the ActivityCaption and TACoS datasets, where it significantly outperforms existing approaches like QSPN and ACRN, achieving retrieval performance improvements across various IoU thresholds. Notably, CMIN achieves R@1, IoU=0.5 scores of 43.40% on ActivityCaption and 24.64% on TACoS, demonstrating robust performance especially in handling complex, long-range interactions in video data.

Theoretical and Practical Implications

Theoretically, this research exemplifies the effectiveness of incorporating syntactic dependency parsing into visual-linguistic tasks, emphasizing the importance of leveraging language structure beyond simple embeddings. Practically, CMIN's architecture is a step forward for applications requiring accurate video segment retrieval, such as content-based video search engines, video summarization tools, and assistive video technologies in various domains.

Future Prospects

Given the dynamic nature of video content and the variability in query formulations, future developments can explore adaptive syntactic parsing mechanisms that can adjust to different linguistic styles or semantic concepts. Additionally, enhancing self-attention mechanisms to be contextually aware across varied temporal resolutions in video data remains an open area for exploration, potentially involving the integration of temporal convolutional networks or advanced temporal encoders.

In conclusion, the CMIN model represents a methodological advancement in cross-modal retrieval tasks, offering insights into the sophisticated interplay between video contexts and natural language queries. As multimedia data proliferates, such models will be critical in refining the precision and relevance of video search systems.