{"type":"ScholarlyArticle","title":"Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition","authors":[{"name":"Yang Wang","slug":"yang-wang","given":"Yang","family":"Wang","affiliation":"Key Laboratory of Social Computing and Cognitive Intelligence , Dalian University of Technology; National Univerisity of Singapore"},{"name":"Haiyang Mei","slug":"haiyang-mei","given":"Haiyang","family":"Mei","affiliation":"National Univerisity of Singapore; Key Laboratory of Social Computing and Cognitive Intelligence , Dalian University of Technology"},{"name":"Qirui Bao","slug":"qirui-bao","given":"Qirui","family":"Bao","affiliation":"Key Laboratory of Social Computing and Cognitive Intelligence , Dalian University of Technology"},{"name":"Ziqi Wei","slug":"ziqi-wei","given":"Ziqi","family":"Wei","affiliation":"Institute of Automation , Chinese Academy of Sciences"},{"name":"Mike Zheng Shou","slug":"mike-zheng-shou","given":"Mike Zheng","family":"Shou","affiliation":"National Univerisity of Singapore; CUHK-Shenzhen)"},{"name":"Haizhou Li","slug":"haizhou-li","given":"Haizhou","family":"Li","affiliation":"The Chinese University of Hong Kong , Shenzhen (; National Univerisity of Singapore"},{"name":"Bo Dong","slug":"bo-dong","given":"Bo","family":"Dong","affiliation":"Independent Researcher"},{"name":"Xin Yang","slug":"xin-yang","given":"Xin","family":"Yang","affiliation":"Key Laboratory of Social Computing and Cognitive Intelligence , Dalian University of Technology"}],"abstract":"We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an event-frame multimodal teacher network. The core strength of this approach is its ability to utilize the ample, coarser temporal cues found in conventional frames for effective emotion recognition. Consequently, our method adeptly interprets both temporal and spatial information from the conventional frame domain, eliminating the need for specialized sensing devices, e.g., event-based camera. The effectiveness of our approach is thoroughly demonstrated using both existing and our compiled single-eye emotion recognition datasets, achieving unparalleled performance in accuracy and efficiency over existing state-of-the-art methods.","doi":"10.24963/ijcai.2024/350","url":"https://ijcai.org/proceedings/2024/350","pdf":"https://ijcai.org/proceedings/2024/0350.pdf","markdown":"https://ijcai.org/proceedings/2024/350.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/350","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":"Humans and AI","track":"Main Track","number":350,"pages":{"first":3160,"last":3168},"keywords":["Humans and AI: HAI: Cognitive modeling","Humans and AI: HAI: Cognitive systems"],"videos":[],"references":[{"position":1,"raw":"[Aifanti et al., 2010] Niki Aifanti, Christos Papachristou, and Anastasios Delopoulos. 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