# Generalized Inter-class Loss for Gait Recognition

arXiv: https://arxiv.org/abs/2210.06779 · HTML: https://mlsanity.com/paper/2210.06779/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 72 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 21 of 21)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation](https://mlsanity.com/paper/2011.01461/) (2020)
- [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation](https://mlsanity.com/paper/2004.11500/) (2020)
- [von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification](https://mlsanity.com/paper/1706.04264/) (2017)
- [On Learning Disentangled Representations for Gait Recognition](https://mlsanity.com/paper/1909.03051/) (2019)
- [JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping](https://mlsanity.com/paper/2005.08625/) (2020)
- [Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks](https://mlsanity.com/paper/2004.04674/) (2020)
- [Rethinking Feature Discrimination and Polymerization for Large-scale Recognition](https://mlsanity.com/paper/1710.00870/) (2017)
- [View-Invariant Gait Recognition with Attentive Recurrent Learning of Partial Representations](https://mlsanity.com/paper/2010.09092/) (2020)
- [RealGait: Gait Recognition for Person Re-Identification](https://mlsanity.com/paper/2201.04806/) (2022)
- [Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification](https://mlsanity.com/paper/2006.04991/) (2020)
- [Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning](https://mlsanity.com/paper/2204.07302/) (2022)
- [SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition](https://mlsanity.com/paper/2103.14811/) (2021)

## Built on by (most cited first, 8 of 8)

- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [Gait Recognition in Large-scale Free Environment via Single LiDAR](https://mlsanity.com/paper/2211.12371/) (2022)
- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)
- [InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation](https://mlsanity.com/paper/2302.09886/) (2023)
- [Evidence-based Match-status-Aware Gait Recognition for Out-of-Gallery Gait Identification](https://mlsanity.com/paper/2211.08007/) (2022)
- [Silhouette-based Gait Foundation Model](https://mlsanity.com/paper/2512.00691/) (2025)
- [SilLang: Improving Gait Recognition with Silhouette Language Encoding](https://mlsanity.com/paper/2603.23976/) (2026)

## Abstract (arXiv)

Gait recognition is a unique biometric technique that can be performed at a long distance non-cooperatively and has broad applications in public safety and intelligent traffic systems. Previous gait works focus more on minimizing the intra-class variance while ignoring the significance in constraining inter-class variance. To this end, we propose a generalized inter-class loss which resolves the inter-class variance from both sample-level feature distribution and class-level feature distribution. Instead of equal penalty strength on pair scores, the proposed loss optimizes sample-level inter-class feature distribution by dynamically adjusting the pairwise weight. Further, in class-level distribution, generalized inter-class loss adds a constraint on the uniformity of inter-class feature distribution, which forces the feature representations to approximate a hypersphere and keep maximal inter-class variance. In addition, the proposed method automatically adjusts the margin between classes which enables the inter-class feature distribution to be more flexible. The proposed method can be generalized to different gait recognition networks and achieves significant improvements. We conduct a series of experiments on CASIA-B and OUMVLP, and the experimental results show that the proposed loss can significantly improve the performance and achieves the state-of-the-art performances.