# Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification

arXiv: https://arxiv.org/abs/2104.13780 · HTML: https://mlsanity.com/paper/2104.13780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 52 references that are in the map.
- First posted on arXiv 28 Apr 2021.

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

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Re-ranking Person Re-identification with k-reciprocal Encoding](https://mlsanity.com/paper/1701.08398/) (2017)
- [Coupled Generative Adversarial Networks](https://mlsanity.com/paper/1606.07536/) (2016)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [Unsupervised Cross-Domain Image Generation](https://mlsanity.com/paper/1611.02200/) (2016)
- [A Discriminatively Learned CNN Embedding for Person Re-identification](https://mlsanity.com/paper/1611.05666/) (2016)
- [Unsupervised Person Re-identification: Clustering and Fine-tuning](https://mlsanity.com/paper/1705.10444/) (2017)
- [Camera Style Adaptation for Person Re-identification](https://mlsanity.com/paper/1711.10295/) (2017)
- [Multi-Level Factorisation Net for Person Re-Identification](https://mlsanity.com/paper/1803.09132/) (2018)
- [Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification](https://mlsanity.com/paper/1710.06555/) (2017)
- [Attention-Aware Compositional Network for Person Re-identification](https://mlsanity.com/paper/1805.03344/) (2018)
- [Domain Adaptation through Synthesis for Unsupervised Person Re-identification](https://mlsanity.com/paper/1804.10094/) (2018)
- [End-to-End Comparative Attention Networks for Person Re-identification](https://mlsanity.com/paper/1606.04404/) (2016)
- [Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification](https://mlsanity.com/paper/1604.07528/) (2016)
- [Multi-component Image Translation for Deep Domain Generalization](https://mlsanity.com/paper/1812.08974/) (2018)
- [Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification](https://mlsanity.com/paper/1607.08378/) (2016)
- [Deep Feature Learning with Relative Distance Comparison for Person Re-identification](https://mlsanity.com/paper/1512.03622/) (2015)
- [Pixel-Level Domain Transfer](https://mlsanity.com/paper/1603.07442/) (2016)
- [Scalable Person Re-identification on Supervised Smoothed Manifold](https://mlsanity.com/paper/1703.08359/) (2017)
- [Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding](https://mlsanity.com/paper/1901.10177/) (2019)
- [Resource Aware Person Re-identification across Multiple Resolutions](https://mlsanity.com/paper/1805.08805/) (2018)
- [Embedding Deep Metric for Person Re-identication A Study Against Large Variations](https://mlsanity.com/paper/1611.00137/) (2016)
- [Deep Ranking for Person Re-identification via Joint Representation Learning](https://mlsanity.com/paper/1505.06821/) (2015)
- [PersonNet: Person Re-identification with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1601.07255/) (2016)
- [A Multi-task Deep Network for Person Re-identification](https://mlsanity.com/paper/1607.05369/) (2016)

## Built on by (most cited first, 1 of 1)

- [Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization](https://mlsanity.com/paper/2108.08995/) (2021)

## Abstract (arXiv)

In a real world environment, person re-identification (Re-ID) is a challenging task due to variations in lighting conditions, viewing angles, pose and occlusions. Despite recent performance gains, current person Re-ID algorithms still suffer heavily when encountering these variations. To address this problem, we propose a semantic consistency and identity mapping multi-component generative adversarial network (SC-IMGAN) which provides style adaptation from one to many domains. To ensure that transformed images are as realistic as possible, we propose novel identity mapping and semantic consistency losses to maintain identity across the diverse domains. For the Re-ID task, we propose a joint verification-identification quartet network which is trained with generated and real images, followed by an effective quartet loss for verification. Our proposed method outperforms state-of-the-art techniques on six challenging person Re-ID datasets: CUHK01, CUHK03, VIPeR, PRID2011, iLIDS and Market-1501.