{"type":"ScholarlyArticle","title":"Task-Agnostic Self-Distillation for Few-Shot Action Recognition","authors":[{"name":"Bin Zhang","slug":"bin-zhang","given":"Bin","family":"Zhang","affiliation":"Zhejiang University of Technology"},{"name":"Yuanjie Dang","slug":"yuanjie-dang","given":"Yuanjie","family":"Dang","affiliation":"Zhejiang University of Technology"},{"name":"Peng Chen","slug":"peng-chen","given":"Peng","family":"Chen","affiliation":"Zhejiang University of Technology"},{"name":"Ronghua Liang","slug":"ronghua-liang","given":"Ronghua","family":"Liang","affiliation":"Zhejiang University of Technology"},{"name":"Nan Gao","slug":"nan-gao","given":"Nan","family":"Gao","affiliation":"Zhejiang University of Technology"},{"name":"Ruohong Huan","slug":"ruohong-huan","given":"Ruohong","family":"Huan","affiliation":"Zhejiang University of Technology"},{"name":"Xiaofei He","slug":"xiaofei-he","given":"Xiaofei","family":"He","affiliation":"Zhejiang University"}],"abstract":"Task-oriented matching is one of the core aspects of few-shot Action Recognition. Most previous works leverage the metric features within the support and query sets of individual tasks, without considering the metric information across different matching tasks. This oversight represents a significant limitation in this task. Specifically, the task-specific metric feature can decrease the generalization ability and ignore the general matching feature applicable across different tasks. To address these challenges, we propose a novel meta-distillation framework for few-shot action recognition that learns the task-agnostic metric features and generalizes them to different tasks. First, to extract the task-agnostic metric information, we design a task-based self-distillation framework to learn the metric features from the training process progressively. Additionally, to enable the model with fine-grained matching capabilities, we design a multi-dimensional distillation module that extracts more detailed relations from the temporal, spatial, and channel dimensions within video pairs and improves the representative performance of metric features for each individual task. After that, the few-shot predictions can be obtained by feeding the embedded task-agnostic metric features to a common feature matcher. Extensive experimental results on standard datasets demonstrate our method’s superior performance compared to existing state-of-the-art methods.","doi":"10.24963/ijcai.2024/600","url":"https://ijcai.org/proceedings/2024/600","pdf":"https://ijcai.org/proceedings/2024/0600.pdf","markdown":"https://ijcai.org/proceedings/2024/600.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/600","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":"Machine Learning","track":"Main Track","number":600,"pages":{"first":5425,"last":5433},"keywords":["Machine Learning: ML: Few-shot learning","Computer Vision: CV: Action and behavior recognition","Machine Learning: ML: Meta-learning"],"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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