# Batch Coherence-Driven Network for Part-aware Person Re-Identification

arXiv: https://arxiv.org/abs/2009.09692 · HTML: https://mlsanity.com/paper/2009.09692/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification](https://mlsanity.com/paper/2107.12666/) (2021)
- [Learning Granularity-Unified Representations for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2207.07802/) (2022)
- [Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation](https://mlsanity.com/paper/2210.04524/) (2022)
- [Quality-aware Part Models for Occluded Person Re-identification](https://mlsanity.com/paper/2201.00107/) (2022)
- [Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification](https://mlsanity.com/paper/2108.09682/) (2021)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [Towards Hard-Positive Query Mining for DETR-based Human-Object Interaction Detection](https://mlsanity.com/paper/2207.05293/) (2022)
- [Style Interleaved Learning for Generalizable Person Re-identification](https://mlsanity.com/paper/2207.03132/) (2022)
- [Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval](https://mlsanity.com/paper/2303.09230/) (2023)
- [Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification](https://mlsanity.com/paper/2107.08228/) (2021)
- [Context Sensing Attention Network for Video-based Person Re-identification](https://mlsanity.com/paper/2207.02631/) (2022)
- [Prototype-guided Cross-modal Completion and Alignment for Incomplete Text-based Person Re-identification](https://mlsanity.com/paper/2309.17104/) (2023)
- [Disentangled Interaction Representation for One-Stage Human-Object Interaction Detection](https://mlsanity.com/paper/2312.01713/) (2023)

## Abstract (arXiv)

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.