# Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification

arXiv: https://arxiv.org/abs/2212.09498 · HTML: https://mlsanity.com/paper/2212.09498/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Joint Discriminative and Generative Learning for Person Re-identification](https://mlsanity.com/paper/1904.07223/) (2019)
- [Relation-Aware Global Attention for Person Re-identification](https://mlsanity.com/paper/1904.02998/) (2019)
- [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)
- [Learning Debiased Representation via Disentangled Feature Augmentation](https://mlsanity.com/paper/2107.01372/) (2021)
- [Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification](https://mlsanity.com/paper/2007.10315/) (2020)
- [Appearance-Preserving 3D Convolution for Video-based Person Re-identification](https://mlsanity.com/paper/2007.08434/) (2020)
- [Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification](https://mlsanity.com/paper/1803.09882/) (2018)
- [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)
- [Temporal Complementary Learning for Video Person Re-Identification](https://mlsanity.com/paper/2007.09357/) (2020)
- [STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification](https://mlsanity.com/paper/1811.04129/) (2018)
- [BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification](https://mlsanity.com/paper/2104.14783/) (2021)
- [Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification](https://mlsanity.com/paper/2003.12224/) (2020)
- [Multi-scale 3D Convolution Network for Video Based Person Re-Identification](https://mlsanity.com/paper/1811.07468/) (2018)
- [DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation](https://mlsanity.com/paper/2103.13447/) (2021)
- [Video-based Person Re-identification with Spatial and Temporal Memory Networks](https://mlsanity.com/paper/2108.09039/) (2021)
- [PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes](https://mlsanity.com/paper/1909.06826/) (2019)
- [Residual Relaxation for Multi-view Representation Learning](https://mlsanity.com/paper/2110.15348/) (2021)
- [Learning Debiased and Disentangled Representations for Semantic Segmentation](https://mlsanity.com/paper/2111.00531/) (2021)
- [Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning](https://mlsanity.com/paper/2212.04712/) (2022)

## Built on by (most cited first, 5 of 5)

- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID](https://mlsanity.com/paper/2508.05038/) (2025)
- [Coarse Attribute Prediction with Task Agnostic Distillation for Real World Clothes Changing ReID](https://mlsanity.com/paper/2505.12580/) (2025)
- [Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessment](https://mlsanity.com/paper/2407.09806/) (2024)
- [Spatial-Temporal Expert Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2607.01353/) (2026)

## Abstract (arXiv)

In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames. Existing methods tend to focus only on how to use temporal information, which often leads to networks being fooled by similar appearances and same backgrounds. In this paper, we propose a Disentanglement and Switching and Aggregation Network (DSANet), which segregates the features representing identity and features based on camera characteristics, and pays more attention to ID information. We also introduce an auxiliary task that utilizes a new pair of features created through switching and aggregation to increase the network's capability for various camera scenarios. Furthermore, we devise a Target Localization Module (TLM) that extracts robust features against a change in the position of the target according to the frame flow and a Frame Weight Generation (FWG) that reflects temporal information in the final representation. Various loss functions for disentanglement learning are designed so that each component of the network can cooperate while satisfactorily performing its own role. Quantitative and qualitative results from extensive experiments demonstrate the superiority of DSANet over state-of-the-art methods on three benchmark datasets.