Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos

• URL: http://arxiv.org/abs/2008.08257v1
• Date: Wed, 19 Aug 2020 04:42:46 GMT
• Title: Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
• Authors: Zhu Zhang, Zhijie Lin, Zhou Zhao, Jieming Zhu and Xiuqiang He
• Abstract summary: Video moment retrieval aims to localize the target moment in a video according to the given sentence. Most existing weak-supervised methods apply a MIL-based framework to develop inter-sample confrontment. We propose a novel Regularized Two-Branch Proposal Network to simultaneously consider the inter-sample and intra-sample confrontments.
• Score: 108.55320735031721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video moment retrieval aims to localize the target moment in an video according to the given sentence. The weak-supervised setting only provides the video-level sentence annotations during training. Most existing weak-supervised methods apply a MIL-based framework to develop inter-sample confrontment, but ignore the intra-sample confrontment between moments with semantically similar contents. Thus, these methods fail to distinguish the target moment from plausible negative moments. In this paper, we propose a novel Regularized Two-Branch Proposal Network to simultaneously consider the inter-sample and intra-sample confrontments. Concretely, we first devise a language-aware filter to generate an enhanced video stream and a suppressed video stream. We then design the sharable two-branch proposal module to generate positive proposals from the enhanced stream and plausible negative proposals from the suppressed one for sufficient confrontment. Further, we apply the proposal regularization to stabilize the training process and improve model performance. The extensive experiments show the effectiveness of our method. Our code is released at here.

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