Deconfounded Video Moment Retrieval with Causal Intervention (2106.01534v1)

Abstract: We tackle the task of video moment retrieval (VMR), which aims to localize a specific moment in a video according to a textual query. Existing methods primarily model the matching relationship between query and moment by complex cross-modal interactions. Despite their effectiveness, current models mostly exploit dataset biases while ignoring the video content, thus leading to poor generalizability. We argue that the issue is caused by the hidden confounder in VMR, {i.e., temporal location of moments}, that spuriously correlates the model input and prediction. How to design robust matching models against the temporal location biases is crucial but, as far as we know, has not been studied yet for VMR. To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction. Specifically, we develop a Deconfounded Cross-modal Matching (DCM) method to remove the confounding effects of moment location. It first disentangles moment representation to infer the core feature of visual content, and then applies causal intervention on the disentangled multimodal input based on backdoor adjustment, which forces the model to fairly incorporate each possible location of the target into consideration. Extensive experiments clearly show that our approach can achieve significant improvement over the state-of-the-art methods in terms of both accuracy and generalization (Codes: \color{blue}{\url{https://github.com/Xun-Yang/Causal_Video_Moment_Retrieval}}

Deconfounded Video Moment Retrieval with Causal Intervention: A Detailed Analysis

The paper "Deconfounded Video Moment Retrieval with Causal Intervention" tackles the challenge of Video Moment Retrieval (VMR), a pivotal task in multimedia information retrieval that focuses on identifying specific moments in video clips based on textual queries. Existing methods in this domain have predominantly capitalized on the complex cross-modal interactions between text and video to enhance matching accuracies. However, this approach often leans heavily on dataset biases, particularly the temporal location of moments, which impairs the model’s generalizability to new, unseen data.

The Core Problem

A significant issue identified in this research is the presence of "hidden confounders," particularly the temporal locations of video moments, which spuriously correlate with the input data and the model’s predictions. Such biases result in a model's over-reliance on frequent patterns rather than the actual content of videos, thereby limiting the robustness and accuracy of VMR systems when deployed in real-world scenarios.

Proposed Solution: Deconfounded Cross-modal Matching (DCM)

To address these biases, the authors propose a causality-inspired framework for VMR, which incorporates a structural causal model designed to accurately discern the effect of both queries and video content on prediction outcomes. The Deconfounded Cross-modal Matching (DCM) method is introduced to expunge the negative confounding effects wielded by moment locations. The approach consists of two primary steps:

1. Feature Disentangling: This step involves separating the moment representation to extract the core visual content devoid of location biases. The disentangled features are crucial for reducing the confounding effects of temporal location on the model's learning process.
2. Causal Intervention: Utilizing causal inference techniques, particularly backdoor adjustment, the method intervenes in the multi-modal input. This intervention employs simple calculus to transform the prediction logic, such that the model considers each potential location of the target moment in an unbiased manner.

Experimental Validation

The efficacy of the DCM approach is substantiated through rigorous experimentation across three benchmark datasets: ActivityNet-Captions, Charades-STA, and DiDeMo. These experiments demonstrate that the DCM method not only achieves significant improvements in accuracy compared to state-of-the-art techniques but also exhibits superior generalization capability across different distributions.

The authors also propose the use of Out-of-Distribution (OOD) testing as a more stringent evaluation measure than the commonly employed Independent and Identically Distributed (IID) testing. By manipulating the temporal context at the start and end of test videos, this method effectively challenges the model to maintain performance despite shifts in the distribution of moment annotations.

Implications and Future Directions

The implications of this research are twofold: practically, the method enhances the precision and adaptability of video retrieval systems in dynamic environments; theoretically, it underscores the necessity of leveraging causal modeling to circumvent dataset biases—a lesson extendable to various subfields within artificial intelligence. Future research may expand upon these findings by exploring the intervention of other latent variables and extending the application of causal frameworks to related tasks, such as video content analysis and scene understanding.

In conclusion, the introduction of DCM and the emphasis on causal intervention provide a nuanced understanding and an advanced toolset for tackling biases inherent in video moment retrieval tasks, paving the way for more robust and generalizable multimedia retrieval systems.