{"type":"ScholarlyArticle","title":"Efficient Event Stream Super-Resolution with Recursive Multi-Branch Fusion","authors":[{"name":"Quanmin Liang","slug":"quanmin-liang","given":"Quanmin","family":"Liang","affiliation":"School of Computer Science and Engineering , Sun Yat-Sen University; Peng Cheng Laboratory"},{"name":"Zhilin Huang","slug":"zhilin-huang","given":"Zhilin","family":"Huang","affiliation":"Peng Cheng Laboratory; Shenzhen International Graduate School , Tsinghua University"},{"name":"Xiawu Zheng","slug":"xiawu-zheng","given":"Xiawu","family":"Zheng","affiliation":"Peng Cheng Laboratory"},{"name":"Feidiao Yang","slug":"feidiao-yang","given":"Feidiao","family":"Yang","affiliation":"Peng Cheng Laboratory"},{"name":"Jun Peng","slug":"jun-peng","given":"Jun","family":"Peng","affiliation":"Peng Cheng Laboratory"},{"name":"Kai Huang","slug":"kai-huang","given":"Kai","family":"Huang","affiliation":"School of Computer Science and Engineering , Sun Yat-Sen University"},{"name":"Yonghong Tian","slug":"yonghong-tian","given":"Yonghong","family":"Tian","affiliation":"Peng Cheng Laboratory; Peking University"}],"abstract":"Current Event Stream Super-Resolution (ESR) methods overlook the redundant and complementary information present in positive and negative events within the event stream, employing a direct mixing approach for super-resolution, which may lead to detail loss and inefficiency. To address these issues, we propose an efficient Recursive Multi-Branch Information Fusion Network (RMFNet) that separates positive and negative events for complementary information extraction, followed by mutual supplementation and refinement. Particularly, we introduce Feature Fusion Modules (FFM) and Feature Exchange Modules (FEM). FFM is designed for the fusion of contextual information within neighboring event streams, leveraging the coupling relationship between positive and negative events to alleviate the misleading of noises in the respective branches. FEM efficiently promotes the fusion and exchange of information between positive and negative branches, enabling superior local information enhancement and global information complementation. Experimental results demonstrate that our approach achieves over 17% and 31% improvement on synthetic and real datasets, accompanied by a 2.3x acceleration. Furthermore, we evaluate our method on two downstream event-driven applications, i.e., object recognition and video reconstruction, achieving remarkable results that outperform existing methods. Our code and Supplementary Material are available at https://github.com/Lqm26/RMFNet.","doi":"10.24963/ijcai.2024/119","url":"https://ijcai.org/proceedings/2024/119","pdf":"https://ijcai.org/proceedings/2024/0119.pdf","markdown":"https://ijcai.org/proceedings/2024/119.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/119","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":119,"pages":{"first":1074,"last":1082},"keywords":["Computer Vision: CV: Image and video synthesis and generation","Computer Vision: CV: Other"],"videos":[],"references":[{"position":1,"raw":"[Barchid et al., 2023] Sami Barchid, José Mennesson, and Chaabane Djéraba. 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