  Existing deep learning methods have made significant progress in gait
recognition. Typically, appearance-based models binarize inputs into silhouette
sequences. However, mainstream quantization methods prioritize minimizing task
loss over quantization error, which is detrimental to gait recognition with
binarized inputs. Minor variations in silhouette sequences can be diminished in
the network's intermediate layers due to the accumulation of quantization
errors. To address this, we propose a differentiable soft quantizer, which
better simulates the gradient of the round function during backpropagation.
This enables the network to learn from subtle input perturbations. However, our
theoretical analysis and empirical studies reveal that directly applying the
soft quantizer can hinder network convergence. We further refine the training
strategy to ensure convergence while simulating quantization errors.
Additionally, we visualize the distribution of outputs from different samples
in the feature space and observe significant changes compared to the full
precision network, which harms performance. Based on this, we propose an
Inter-class Distance-guided Distillation (IDD) strategy to preserve the
relative distance between the embeddings of samples with different labels.
Extensive experiments validate the effectiveness of our approach, demonstrating
state-of-the-art accuracy across various settings and datasets. The code will
be made publicly available.
