Visual Spatio-temporal Relation-enhanced Network for Cross-modal Text-Video Retrieval

• URL: http://arxiv.org/abs/2110.15609v1
• Date: Fri, 29 Oct 2021 08:23:40 GMT
• Title: Visual Spatio-temporal Relation-enhanced Network for Cross-modal Text-Video Retrieval
• Authors: Ning Han, Jingjing Chen, Guangyi Xiao, Yawen Zeng, Chuhao Shi, Hao Chen
• Abstract summary: Cross-modal retrieval of texts and videos aims to understand the correspondence between vision and language. We propose a Visual S-temporal Relation-enhanced semantic network (CNN-SRNet), a cross-temporal retrieval framework. Experiments are conducted on both MSR-VTT and MSVD datasets.
• Score: 17.443195531553474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of cross-modal retrieval between texts and videos aims to understand the correspondence between vision and language. Existing studies follow a trend of measuring text-video similarity on the basis of textual and video embeddings. In common practice, video representation is constructed by feeding video frames into 2D/3D-CNN for global visual feature extraction or only learning simple semantic relations by using local-level fine-grained frame regions via graph convolutional network. However, these video representations do not fully exploit spatio-temporal relation among visual components in learning video representations, resulting in their inability to distinguish videos with the same visual components but with different relations. To solve this problem, we propose a Visual Spatio-temporal Relation-enhanced Network (VSR-Net), a novel cross-modal retrieval framework that enhances visual representation with spatio-temporal relations among components. Specifically, visual spatio-temporal relations are encoded using a multi-layer spatio-temporal transformer to learn visual relational features. We combine fine-grained local relation and global features in bridging text-video modalities. Extensive experimental are conducted on both MSR-VTT and MSVD datasets. The results demonstrate the effectiveness of our proposed model.

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