  Existing part-aware person re-identification methods typically employ two
separate steps: namely, body part detection and part-level feature extraction.
However, part detection introduces an additional computational cost and is
inherently challenging for low-quality images. Accordingly, in this work, we
propose a simple framework named Batch Coherence-Driven Network (BCD-Net) that
bypasses body part detection during both the training and testing phases while
still learning semantically aligned part features. Our key observation is that
the statistics in a batch of images are stable, and therefore that batch-level
constraints are robust. First, we introduce a batch coherence-guided channel
attention (BCCA) module that highlights the relevant channels for each
respective part from the output of a deep backbone model. We investigate
channelpart correspondence using a batch of training images, then impose a
novel batch-level supervision signal that helps BCCA to identify part-relevant
channels. Second, the mean position of a body part is robust and consequently
coherent between batches throughout the training process. Accordingly, we
introduce a pair of regularization terms based on the semantic consistency
between batches. The first term regularizes the high responses of BCD-Net for
each part on one batch in order to constrain it within a predefined area, while
the second encourages the aggregate of BCD-Nets responses for all parts
covering the entire human body. The above constraints guide BCD-Net to learn
diverse, complementary, and semantically aligned part-level features. Extensive
experimental results demonstrate that BCDNet consistently achieves
state-of-the-art performance on four large-scale ReID benchmarks.
