# Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification

arXiv: https://arxiv.org/abs/2307.07187 · HTML: https://mlsanity.com/paper/2307.07187/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 87 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 30 of 52)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [Person Re-identification in the Wild](https://mlsanity.com/paper/1604.02531/) (2016)
- [Improving the Robustness of Deep Neural Networks via Stability Training](https://mlsanity.com/paper/1604.04326/) (2016)
- [Style Normalization and Restitution for Generalizable Person Re-identification](https://mlsanity.com/paper/2005.11037/) (2020)
- [Identity-Guided Human Semantic Parsing for Person Re-Identification](https://mlsanity.com/paper/2007.13467/) (2020)
- [Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review](https://mlsanity.com/paper/2005.09830/) (2020)
- [High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification](https://mlsanity.com/paper/2003.08177/) (2020)
- [Interaction-and-Aggregation Network for Person Re-identification](https://mlsanity.com/paper/1907.08435/) (2019)
- [Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark](https://mlsanity.com/paper/1804.01984/) (2018)
- [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)
- [Body Part-Based Representation Learning for Occluded Person Re-Identification](https://mlsanity.com/paper/2211.03679/) (2022)
- [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)

## Built on by (most cited first, 19 of 19)

- [M3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition](https://mlsanity.com/paper/2308.03063/) (2023)
- [CLIP-Driven Semantic Discovery Network for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2401.05806/) (2024)
- [A Versatile Framework for Multi-scene Person Re-identification](https://mlsanity.com/paper/2403.11121/) (2024)
- [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://mlsanity.com/paper/2311.00603/) (2023)
- [Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2402.19026/) (2024)
- [Shape-centered Representation Learning for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2310.17952/) (2023)
- [Learning Comprehensive Representations with Richer Self for Text-to-Image Person Re-Identification](https://mlsanity.com/paper/2310.11210/) (2023)
- [PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders](https://mlsanity.com/paper/2311.04496/) (2023)
- [Part-Attention Based Model Make Occluded Person Re-Identification Stronger](https://mlsanity.com/paper/2404.03443/) (2024)
- [Diverse Semantics-Guided Feature Alignment and Decoupling for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2505.00619/) (2025)
- [Towards Effective and Efficient Adversarial Defense with Diffusion Models for Robust Visual Tracking](https://mlsanity.com/paper/2506.00325/) (2025)
- [Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning](https://mlsanity.com/paper/2509.13161/) (2025)
- [DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2511.04281/) (2025)
- [Large-scale Fully-Unsupervised Re-Identification](https://mlsanity.com/paper/2307.14278/) (2023)
- [Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification](https://mlsanity.com/paper/2402.10435/) (2024)
- [Time-Frequency Analysis of Variable-Length WiFi CSI Signals for Person Re-Identification](https://mlsanity.com/paper/2407.09045/) (2024)
- [CLIP4VI-ReID: Learning Modality-shared Representations via CLIP Semantic Bridge for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2511.10309/) (2025)
- [Language Prompt vs. Image Enhancement: Boosting Object Detection With CLIP in Hazy Environments](https://mlsanity.com/paper/2604.10637/) (2026)
- [Reliability-Aware 3D Geometric Injection for Universal Person Re-identification](https://mlsanity.com/paper/2607.18863/) (2026)

## Abstract (arXiv)

Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion. In this paper, we propose a simple yet effective framework, termed Erasing, Transforming, and Noising Defense Network (ETNDNet), which treats occlusion as a noise disturbance and solves occluded person re-ID from the perspective of adversarial defense. In the proposed ETNDNet, we introduce three strategies: Firstly, we randomly erase the feature map to create an adversarial representation with incomplete information, enabling adversarial learning of identity loss to protect the re-ID system from the disturbance of missing information. Secondly, we introduce random transformations to simulate the position misalignment caused by occlusion, training the extractor and classifier adversarially to learn robust representations immune to misaligned information. Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians, and employ adversarial gaming in the re-ID system to enhance its resistance to occlusion noise. Without bells and whistles, ETNDNet has three key highlights: (i) it does not require any external modules with parameters, (ii) it effectively handles various issues caused by occlusion from obstacles and non-target pedestrians, and (iii) it designs the first GAN-based adversarial defense paradigm for occluded person re-ID. Extensive experiments on five public datasets fully demonstrate the effectiveness, superiority, and practicality of the proposed ETNDNet. The code will be released at urlhttps://github.com/nengdong96/ETNDNet.