Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment Retrieval
This work addresses fine-grained video-text interaction for retrieval tasks, offering an incremental improvement over existing methods.
The paper tackled the problem of information asymmetry in video moment retrieval by integrating an asymmetric co-attention network and momentum contrastive loss, achieving better performance on TACoS and comparable results on ActivityNet Captions with fewer parameters.
Video moment retrieval is a challenging task requiring fine-grained interactions between video and text modalities. Recent work in image-text pretraining has demonstrated that most existing pretrained models suffer from information asymmetry due to the difference in length between visual and textual sequences. We question whether the same problem also exists in the video-text domain with an auxiliary need to preserve both spatial and temporal information. Thus, we evaluate a recently proposed solution involving the addition of an asymmetric co-attention network for video grounding tasks. Additionally, we incorporate momentum contrastive loss for robust, discriminative representation learning in both modalities. We note that the integration of these supplementary modules yields better performance compared to state-of-the-art models on the TACoS dataset and comparable results on ActivityNet Captions, all while utilizing significantly fewer parameters with respect to baseline.