Siamese Alignment Network for Weakly Supervised Video Moment Retrieval
Yunxiao Wang, Meng Liu, Yinwei Wei, Zhiyong Cheng, Yinglong Wang, Liqiang Nie
Abstract
Video moment retrieval, i.e., localizing the specific video moments within a video given a description query, has attracted substantial attention over the past several years. Although great progress has been achieved thus far, most of existing methods are supervised, which require moment-level temporal annotation information. In contrast, weakly-supervised methods which only need video-level annotations remain largely unexplored. In this paper, we propose a novel end-to-end Siamese alignment network for weakly-supervised video moment retrieval. To be specific, we design a multi-scale Siamese module, which could progressively reduce the semantic gap between the visual and textual modality with the Siamese structure. In addition, we present a context-aware multiple instance learning module by considering the influence of adjacent contexts, enhancing the moment-query and video-query alignment simultaneously. By promoting the matching of both moment-level and video-level, our model can effectively improve the retrieval performance, even if only having weak video level annotations. Extensive experiments on two benchmark datasets, i.e., ActivityNet-Captions and Charades-STA, verify the superiority of our model compared with several state-of-the-art baselines.