# Deep Representation Learning with Part Loss for Person Re-Identification

arXiv: https://arxiv.org/abs/1707.00798 · HTML: https://mlsanity.com/paper/1707.00798/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2017.

## Built on by (most cited first, 30 of 44)

- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [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)
- [A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization](https://mlsanity.com/paper/2201.09206/) (2022)
- [AAformer: Auto-Aligned Transformer for Person Re-Identification](https://mlsanity.com/paper/2104.00921/) (2021)
- [Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method](https://mlsanity.com/paper/2101.08533/) (2021)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Region Generation and Assessment Network for Occluded Person Re-Identification](https://mlsanity.com/paper/2309.03558/) (2023)
- [Person Re-Identification with a Locally Aware Transformer](https://mlsanity.com/paper/2106.03720/) (2021)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2108.04527/) (2021)
- [Person Re-identification via Attention Pyramid](https://mlsanity.com/paper/2108.05340/) (2021)
- [OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification](https://mlsanity.com/paper/2109.11159/) (2021)
- [Adaptive Attribute and Structure Subspace Clustering Network](https://mlsanity.com/paper/2109.13742/) (2021)
- [RealGait: Gait Recognition for Person Re-Identification](https://mlsanity.com/paper/2201.04806/) (2022)
- [Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation](https://mlsanity.com/paper/2108.03372/) (2021)
- [Keypoint Message Passing for Video-based Person Re-Identification](https://mlsanity.com/paper/2111.08279/) (2021)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [Unity is Strength: Unifying Convolutional and Transformeral Features for Better Person Re-Identification](https://mlsanity.com/paper/2412.17239/) (2024)
- [Towards Generalizable Person Re-identification with a Bi-stream Generative Model](https://mlsanity.com/paper/2206.09362/) (2022)
- [Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification](https://mlsanity.com/paper/2104.11539/) (2021)
- [Boosting Mobile CNN Inference through Semantic Memory](https://mlsanity.com/paper/2112.02644/) (2021)
- [CLIP-SCGI: Synthesized Caption-Guided Inversion for Person Re-Identification](https://mlsanity.com/paper/2410.09382/) (2024)
- [Exploiting Robust Unsupervised Video Person Re-identification](https://mlsanity.com/paper/2111.05170/) (2021)
- [A Semantic-aware Attention and Visual Shielding Network for Cloth-changing Person Re-identification](https://mlsanity.com/paper/2207.08387/) (2022)
- [Learning Transferable Pedestrian Representation from Multimodal Information Supervision](https://mlsanity.com/paper/2304.05554/) (2023)
- [Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning](https://mlsanity.com/paper/2310.08390/) (2023)
- [A Framework to Enhance Generalization of Deep Metric Learning methods using General Discriminative Feature Learning and Class Adversarial Neural Networks](https://mlsanity.com/paper/2106.06420/) (2021)
- [Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification](https://mlsanity.com/paper/2109.05542/) (2021)

## Abstract (arXiv)

Learning discriminative representations for unseen person images is critical for person Re-Identification (ReID). Most of current approaches learn deep representations in classification tasks, which essentially minimize the empirical classification risk on the training set. As shown in our experiments, such representations commonly focus on several body parts discriminative to the training set, rather than the entire human body. Inspired by the structural risk minimization principle in SVM, we revise the traditional deep representation learning procedure to minimize both the empirical classification risk and the representation learning risk. The representation learning risk is evaluated by the proposed part loss, which automatically generates several parts for an image, and computes the person classification loss on each part separately. Compared with traditional global classification loss, simultaneously considering multiple part loss enforces the deep network to focus on the entire human body and learn discriminative representations for different parts. Experimental results on three datasets, i.e., Market1501, CUHK03, VIPeR, show that our representation outperforms the existing deep representations.