{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/visual-spatio-temporal-relation-enhanced","title":"BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval","arxiv_id":"2110.15609","date":"2021-10-29","proceeding":null,"authors":["Ning Han","Jingjing Chen","Chuhao Shi","Yawen Zeng","Guangyi Xiao","Hao Chen"],"abstract":"The task of text-video retrieval aims to understand the correspondence between language and vision, has gained increasing attention in recent years. Previous studies either adopt off-the-shelf 2D/3D-CNN and then use average/max pooling to directly capture spatial features with aggregated temporal information as global video embeddings, or introduce graph-based models and expert knowledge to learn local spatial-temporal relations. However, the existing methods have two limitations: 1) The global video representations learn video temporal information in a simple average/max pooling manner and do not fully explore the temporal information between every two frames. 2) The graph-based local video representations are handcrafted, it depends heavily on expert knowledge and empirical feedback, which may not be able to effectively mine the higher-level fine-grained visual relations. These limitations result in their inability to distinguish videos with the same visual components but with different relations. To solve this problem, we propose a novel cross-modal retrieval framework, Bi-Branch Complementary Network (BiC-Net), which modifies transformer architecture to effectively bridge text-video modalities in a complementary manner via combining local spatial-temporal relation and global temporal information. Specifically, local video representations are encoded using multiple transformer blocks and additional residual blocks to learn spatio-temporal relation features, calling the module a Spatio-Temporal Residual transformer (SRT). Meanwhile, Global video representations are encoded using a multi-layer transformer block to learn global temporal features. Finally, we align the spatio-temporal relation and global temporal features with the text feature on two embedding spaces for cross-modal text-video retrieval.","url_abs":"https://arxiv.org/abs/2110.15609v3","url_pdf":"https://arxiv.org/pdf/2110.15609v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"visual-spatio-temporal-relation-enhanced","repo_url":"https://github.com/lionel-hing/bic-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"visual-spatio-temporal-relation-enhanced","repo_url":"https://github.com/lionel-hing/vsr-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cross-modal-retrieval","task_name":"Cross-Modal Retrieval"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"video-retrieval","task_name":"Video Retrieval"},{"task_slug":"video-similarity","task_name":"Video Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2110.15609","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}