# Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification

arXiv: https://arxiv.org/abs/2303.10976 · HTML: https://mlsanity.com/paper/2303.10976/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 50 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer](https://mlsanity.com/paper/2106.04095/) (2021)
- [Identity-Guided Human Semantic Parsing for Person Re-Identification](https://mlsanity.com/paper/2007.13467/) (2020)
- [High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification](https://mlsanity.com/paper/2003.08177/) (2020)
- [AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification](https://mlsanity.com/paper/2004.08787/) (2020)
- [Occluded Person Re-identification](https://mlsanity.com/paper/1804.02792/) (2018)
- [Pose-guided Visible Part Matching for Occluded Person ReID](https://mlsanity.com/paper/2004.00230/) (2020)
- [Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer](https://mlsanity.com/paper/2112.02466/) (2021)
- [Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach](https://mlsanity.com/paper/1801.00881/) (2018)
- [Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification](https://mlsanity.com/paper/1904.00537/) (2019)
- [Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification](https://mlsanity.com/paper/1904.04975/) (2019)
- [VRSTC: Occlusion-Free Video Person Re-Identification](https://mlsanity.com/paper/1907.08427/) (2019)
- [AAformer: Auto-Aligned Transformer for Person Re-Identification](https://mlsanity.com/paper/2104.00921/) (2021)
- [Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification](https://mlsanity.com/paper/2107.02380/) (2021)
- [Feature Erasing and Diffusion Network for Occluded Person Re-Identification](https://mlsanity.com/paper/2112.08740/) (2021)
- [Learning Disentangled Representation for Robust Person Re-identification](https://mlsanity.com/paper/1910.12003/) (2019)
- [HAT: Hierarchical Aggregation Transformers for Person Re-identification](https://mlsanity.com/paper/2107.05946/) (2021)
- [Feature Completion for Occluded Person Re-Identification](https://mlsanity.com/paper/2106.12733/) (2021)
- [Dynamic Prototype Mask for Occluded Person Re-Identification](https://mlsanity.com/paper/2207.09046/) (2022)
- [STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification](https://mlsanity.com/paper/1903.07072/) (2019)

## Built on by (most cited first, 6 of 6)

- [Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization](https://mlsanity.com/paper/2409.17977/) (2024)
- [ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-Identification](https://mlsanity.com/paper/2409.20081/) (2024)
- [3DOT: Texture Transfer for 3DGS Objects from a Single Reference Image](https://mlsanity.com/paper/2503.18853/) (2025)
- [Attribute Guidance With Inherent Pseudo-label For Occluded Person Re-identification](https://mlsanity.com/paper/2508.04998/) (2025)
- [Object Re-identification via Spatial-temporal Fusion Networks and Causal Identity Matching](https://mlsanity.com/paper/2408.05558/) (2024)
- [DPM++: Dynamic Masked Metric Learning for Occluded Person Re-identification](https://mlsanity.com/paper/2605.06637/) (2026)

## Abstract (arXiv)

Occluded person re-identification (Re-ID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Many methods use the background as artificial occlusion and rely on attention networks to exclude noisy interference. However, the significant discrepancy between simple background occlusion and realistic occlusion can negatively impact the generalization of the network. To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. Firstly, to imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise, which can distract attention like a realistic occluder, as a more complex form of occlusion. Secondly, to fully exploit these complex occluded images, we develop a Dual-Path Constraint Module (DPC) that can obtain preferable supervision information from holistic images through dual-path interaction. With our proposed method, the network can effectively circumvent a wide variety of occlusions using the basic ViT baseline. Comprehensive experimental evaluations conducted on person re-ID benchmarks demonstrate the superiority of ADP over state-of-the-art methods.