The Devil is in the Spurious Correlations: Boosting Moment Retrieval with Dynamic Learning

TL;DR

This paper introduces a dynamic learning approach to improve video moment retrieval by reducing spurious correlations between queries and background frames, achieving state-of-the-art results.

Contribution

It proposes a novel video synthesis and text-dynamics interaction method to mitigate spurious correlations in transformer-based moment retrieval models.

Findings

Significant performance improvement on QVHighlights and Charades-STA benchmarks.

Effective reduction of background over-association in moment prediction.

Demonstrated generalization across different architectures.

Abstract

Given a textual query along with a corresponding video, the objective of moment retrieval aims to localize the moments relevant to the query within the video. While commendable results have been demonstrated by existing transformer-based approaches, predicting the accurate temporal span of the target moment is still a major challenge. This paper reveals that a crucial reason stems from the spurious correlation between the text query and the moment context. Namely, the model makes predictions by overly associating queries with background frames rather than distinguishing target moments. To address this issue, we propose a dynamic learning approach for moment retrieval, where two strategies are designed to mitigate the spurious correlation. First, we introduce a novel video synthesis approach to construct a dynamic context for the queried moment, enabling the model to attend to the target moment of the corresponding query across dynamic backgrounds. Second, to alleviate the over-association with backgrounds, we enhance representations temporally by incorporating text-dynamics interaction, which encourages the model to align text with target moments through complementary dynamic representations. With the proposed method, our model significantly alleviates the spurious correlation issue in moment retrieval and establishes new state-of-the-art performance on two popular benchmarks, \ie, QVHighlights and Charades-STA. In addition, detailed ablation studies and evaluations across different architectures demonstrate the generalization and effectiveness of the proposed strategies. Our code will be publicly available.