{"type":"ScholarlyArticle","title":"Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network for Spatial-Temporal Action Localization","authors":[{"name":"Jun Yu","slug":"jun-yu","given":"Jun","family":"Yu","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence , Hefei , China; iFLYTEK Co., Ltd. , Hefei , China"},{"name":"Yingshuai Zheng","slug":"yingshuai-zheng","given":"Yingshuai","family":"Zheng","affiliation":"iFLYTEK Co., Ltd. , Hefei , China"},{"name":"Shulan Ruan","slug":"shulan-ruan","given":"Shulan","family":"Ruan","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence , Hefei , China"},{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence , Hefei , China"},{"name":"Zhiyuan Cheng","slug":"zhiyuan-cheng","given":"Zhiyuan","family":"Cheng","affiliation":"iFLYTEK Co., Ltd. , Hefei , China"},{"name":"Jinze Wu","slug":"jinze-wu","given":"Jinze","family":"Wu","affiliation":"iFLYTEK Co., Ltd. , Hefei , China"}],"abstract":"The key to video action detection lies in the understanding of interaction between persons and background objects in a video. Current methods usually employ object detectors to extract objects directly or use grid features to represent objects in the environment, which underestimate the great potential of multi-scale context information (e.g., objects and scenes of different sizes). How to exactly represent the multi-scale context and make full utilization of it still remains an unresolved challenge for spatial-temporal action localization. In this paper, we propose a novel Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network (AMCRNet) that extracts multi-scale context through multiple pooling layers with different sizes. Specifically, we develop an Interactive Relation Extraction module to model the higher-order relation between the target person and the context (e.g., other persons and objects). Along this line, we further propose a History Feature Bank and Interaction method to achieve better performance by modeling such relation across continuing video clips. Extensive experimental results on AVA2.2 and UCF101-24 demonstrate the superiority and rationality of our proposed AMCRNet.","doi":"10.24963/ijcai.2023/186","url":"https://ijcai.org/proceedings/2023/186","pdf":"https://ijcai.org/proceedings/2023/0186.pdf","markdown":"https://ijcai.org/proceedings/2023/186.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/186","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":186,"pages":{"first":1676,"last":1685},"keywords":["Computer Vision: CV: Machine learning for vision","Computer Vision: CV: Video analysis and understanding","Computer Vision: CV: Action and behavior recognition"],"videos":[],"references":[{"position":1,"raw":"[Arnab et al., 2021] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. 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