{"type":"ScholarlyArticle","title":"GTR: A Grafting-Then-Reassembling Framework for Dynamic Scene Graph Generation","authors":[{"name":"Jiafeng Liang","slug":"jiafeng-liang","given":"Jiafeng","family":"Liang","affiliation":"Harbin Institute of Technology , Harbin , China"},{"name":"Yuxin Wang","slug":"yuxin-wang","given":"Yuxin","family":"Wang","affiliation":"Harbin Institute of Technology , Harbin , China"},{"name":"Zekun Wang","slug":"zekun-wang","given":"Zekun","family":"Wang","affiliation":"Harbin Institute of Technology , Harbin , China"},{"name":"Ming Liu","slug":"ming-liu","given":"Ming","family":"Liu","affiliation":"Harbin Institute of Technology , Harbin , China; Peng Cheng Laboratory , Shenzhen , China"},{"name":"Ruiji Fu","slug":"ruiji-fu","given":"Ruiji","family":"Fu","affiliation":"Kuaishou Technology , Beijing , China"},{"name":"Zhongyuan Wang","slug":"zhongyuan-wang","given":"Zhongyuan","family":"Wang","affiliation":"Kuaishou Technology , Beijing , China"},{"name":"Bing Qin","slug":"bing-qin","given":"Bing","family":"Qin","affiliation":"Harbin Institute of Technology , Harbin , China; Peng Cheng Laboratory , Shenzhen , China"}],"abstract":"Dynamic scene graph generation aims to identify visual relationships (subject-predicate-object) in frames based on spatio-temporal contextual information in the video. Previous work implicitly models the spatio-temporal interaction simultaneously, which leads to entanglement of spatio-temporal contextual information. To this end, we propose a Grafting-Then-Reassembling framework (GTR), which explicitly extracts intra-frame spatial information and inter-frame temporal information in two separate stages to decouple spatio-temporal contextual information. Specifically, we first graft a static scene graph generation model to generate static visual relationships within frames. Then we propose the temporal dependency model to extract the temporal dependencies across frames, and explicitly reassemble static visual relationships into dynamic scene graphs. Experimental results show that GTR achieves the state-of-the-art performance on Action Genome dataset. Further analyses reveal that the reassembling stage is crucial to the success of our framework.","doi":"10.24963/ijcai.2023/131","url":"https://ijcai.org/proceedings/2023/131","pdf":"https://ijcai.org/proceedings/2023/0131.pdf","markdown":"https://ijcai.org/proceedings/2023/131.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/131","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":131,"pages":{"first":1177,"last":1185},"keywords":["Computer Vision: CV: Video analysis and understanding","Natural Language Processing: NLP: Information extraction"],"videos":[],"references":[{"position":1,"raw":"[Arnab et al., 2021] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. 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