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.

使用软量化器和距离引导蒸馏策略，在步态识别中实现关键词所示的深度学习方法，并验证了其在各种设置和数据集上的最先进准确性。

QGait: 用于步态识别的准确量化和二值化输入

QGait: Toward Accurate Quantization for Gait Recognition with Binarized  Input

A popular approach to learning encoders for lossy compression is to use
additive uniform noise during training as a differentiable approximation to
test-time quantization. We demonstrate that a uniform noise channel can also be
implemented at test time using universal quantization (Ziv, 1985). This allows
us to eliminate the mismatch between training and test phases while maintaining
a completely differentiable loss function. Implementing the uniform noise
channel is a special case of the more general problem of communicating a
sample, which we prove is computationally hard if we do not make assumptions
about its distribution. However, the uniform special case is efficient as well
as easy to implement and thus of great interest from a practical point of view.
Finally, we show that quantization can be obtained as a limiting case of a soft
quantizer applied to the uniform noise channel, bridging compression with and
without quantization.

通过实现一种统一的量化方式，消除训练和测试阶段的不匹配，使我们能够使用可完全区分的损失函数学习编码器。当遇到没有关于分布的假设时，这种方法的通用性可能会面临计算上的困难。最后，我们还展示了量化如何通过软量化实现，从而将压缩与无量化桥接起来。