MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition

• URL: http://arxiv.org/abs/2306.03445v1
• Date: Tue, 6 Jun 2023 06:53:05 GMT
• Title: MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition
• Authors: Huanzhang Dou, Pengyi Zhang, Wei Su, Yunlong Yu, and Xi Li
• Abstract summary: We develop a novel MetaGait that learns to learn an omni sample adaptive representation. We leverage the meta-knowledge across the entire process, where Meta Triple Attention and Meta Temporal Pooling are presented. Extensive experiments demonstrate the state-of-the-art performance of the proposed MetaGait.
• Score: 16.26377062742576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition, which aims at identifying individuals by their walking patterns, has recently drawn increasing research attention. However, gait recognition still suffers from the conflicts between the limited binary visual clues of the silhouette and numerous covariates with diverse scales, which brings challenges to the model's adaptiveness. In this paper, we address this conflict by developing a novel MetaGait that learns to learn an omni sample adaptive representation. Towards this goal, MetaGait injects meta-knowledge, which could guide the model to perceive sample-specific properties, into the calibration network of the attention mechanism to improve the adaptiveness from the omni-scale, omni-dimension, and omni-process perspectives. Specifically, we leverage the meta-knowledge across the entire process, where Meta Triple Attention and Meta Temporal Pooling are presented respectively to adaptively capture omni-scale dependency from spatial/channel/temporal dimensions simultaneously and to adaptively aggregate temporal information through integrating the merits of three complementary temporal aggregation methods. Extensive experiments demonstrate the state-of-the-art performance of the proposed MetaGait. On CASIA-B, we achieve rank-1 accuracy of 98.7%, 96.0%, and 89.3% under three conditions, respectively. On OU-MVLP, we achieve rank-1 accuracy of 92.4%.

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