  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%.
