{"type":"ScholarlyArticle","title":"ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition","authors":[{"name":"Mengqi Xue","slug":"mengqi-xue","given":"Mengqi","family":"Xue","affiliation":"Hangzhou City University"},{"name":"Qihan Huang","slug":"qihan-huang","given":"Qihan","family":"Huang","affiliation":"Zhejiang University"},{"name":"Haofei Zhang","slug":"haofei-zhang","given":"Haofei","family":"Zhang","affiliation":"Zhejiang University"},{"name":"Jingwen Hu","slug":"jingwen-hu","given":"Jingwen","family":"Hu","affiliation":"Zhejiang University"},{"name":"Jie Song","slug":"jie-song","given":"Jie","family":"Song","affiliation":"Zhejiang University; ZJU-Bangsun Joint Research Center"},{"name":"Mingli Song","slug":"mingli-song","given":"Mingli","family":"Song","affiliation":"Hangzhou City University; Zhejiang University; ZJU-Bangsun Joint Research Center"},{"name":"Canghong Jin","slug":"canghong-jin","given":"Canghong","family":"Jin","affiliation":"Hangzhou City University"}],"abstract":"Prototypical part network (ProtoPNet) and its variants have drawn wide attention and been applied to various tasks due to their inherent self-explanatory property. Previous ProtoPNets are primarily built upon convolutional neural networks (CNNs). Therefore, it is natural to investigate whether these explainable methods can be advantageous for the recently emerged Vision Transformers (ViTs). However, directly utilizing ViT-backed models as backbones can lead to prototypes paying excessive attention to background positions rather than foreground objects (i.e., the “distraction” problem). To address the problem, this paper proposes prototypical part Transformer (ProtoPFormer) for interpretable image recognition. Based the architectural characteristics of ViTs, we modify the original ProtoPNet by creating separate global and local branches, each accompanied by corresponding prototypes that can capture and highlight representative holistic and partial features. Specifically, the global prototypes can guide local prototypes to concentrate on the foreground and effectively suppress the background influence. Subsequently, local prototypes are explicitly supervised to concentrate on different discriminative visual parts. Finally, the two branches mutually correct each other and jointly make the final decisions. Moreover, extensive experiments demonstrate that ProtoPFormer can consistently achieve superior performance on accuracy, visualization results, and quantitative interpretability evaluation over the state-of-the-art (SOTA) baselines. Our code has been released at https://github.com/zju-vipa/ProtoPFormer.","doi":"10.24963/ijcai.2024/168","url":"https://ijcai.org/proceedings/2024/168","pdf":"https://ijcai.org/proceedings/2024/0168.pdf","markdown":"https://ijcai.org/proceedings/2024/168.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/168","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":168,"pages":{"first":1516,"last":1524},"keywords":["Computer Vision: CV: Interpretability and transparency","Computer Vision: CV: Representation learning","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Abnar and Zuidema, 2020] Samira Abnar and Willem Zuidema. 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