# Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification

arXiv: https://arxiv.org/abs/2204.07943 · HTML: https://mlsanity.com/paper/2204.07943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 18 of 18)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [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)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition](https://mlsanity.com/paper/1503.02101/) (2015)
- [Revisiting Small Batch Training for Deep Neural Networks](https://mlsanity.com/paper/1804.07612/) (2018)
- [Vehicle Re-identification with Viewpoint-aware Metric Learning](https://mlsanity.com/paper/1910.04104/) (2019)
- [The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification](https://mlsanity.com/paper/2004.06271/) (2020)
- [A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification](https://mlsanity.com/paper/1905.03397/) (2019)
- [Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network](https://mlsanity.com/paper/2008.11423/) (2020)
- [Heterogeneous Relational Complement for Vehicle Re-identification](https://mlsanity.com/paper/2109.07894/) (2021)
- [Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals](https://mlsanity.com/paper/1708.03918/) (2017)
- [Vehicle Re-Identification: an Efficient Baseline Using Triplet Embedding](https://mlsanity.com/paper/1901.01015/) (2019)
- [VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera](https://mlsanity.com/paper/2004.09164/) (2020)
- [A Strong Baseline for Vehicle Re-Identification](https://mlsanity.com/paper/2104.10850/) (2021)
- [Revisiting the Loss Weight Adjustment in Object Detection](https://mlsanity.com/paper/2103.09488/) (2021)

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

- [T2I-VeRW: Part-level Fine-grained Perception for Text-to-Image Vehicle Retrieval](https://mlsanity.com/paper/2605.06012/) (2026)

## Abstract (arXiv)

In this paper, we propose a Global-Supervised Contrastive loss and a view-aware-based post-processing (VABPP) method for the field of vehicle re-identification. The traditional supervised contrastive loss calculates the distances of features within the batch, so it has the local attribute. While the proposed Global-Supervised Contrastive loss has new properties and has good global attributes, the positive and negative features of each anchor in the training process come from the entire training set. The proposed VABPP method is the first time that the view-aware-based method is used as a post-processing method in the field of vehicle re-identification. The advantages of VABPP are that, first, it is only used during testing and does not affect the training process. Second, as a post-processing method, it can be easily integrated into other trained re-id models. We directly apply the view-pair distance scaling coefficient matrix calculated by the model trained in this paper to another trained re-id model, and the VABPP method greatly improves its performance, which verifies the feasibility of the VABPP method.