TY - JOUR AU - Lin, Zhijie AU - Zhao, Zhou AU - Zhang, Zhu AU - Wang, Qi AU - Liu, Huasheng PY - 2020/04/03 Y2 - 2024/03/28 TI - Weakly-Supervised Video Moment Retrieval via Semantic Completion Network JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 07 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v34i07.6820 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6820 SP - 11539-11546 AB - <p>Video moment retrieval is to search the moment that is most relevant to the given natural language query. Existing methods are mostly trained in a fully-supervised setting, which requires the full annotations of temporal boundary for each query. However, manually labeling the annotations is actually time-consuming and expensive. In this paper, we propose a novel weakly-supervised moment retrieval framework requiring only coarse video-level annotations for training. Specifically, we devise a proposal generation module that aggregates the context information to generate and score all candidate proposals in one single pass. We then devise an algorithm that considers both exploitation and exploration to select top-K proposals. Next, we build a semantic completion module to measure the semantic similarity between the selected proposals and query, compute reward and provide feedbacks to the proposal generation module for scoring refinement. Experiments on the ActivityCaptions and Charades-STA demonstrate the effectiveness of our proposed method.</p> ER -