# Study on Aspect Ratio Variability toward Robustness of Vision Transformer-based Vehicle Re-identification

arXiv: https://arxiv.org/abs/2407.07842 · HTML: https://mlsanity.com/paper/2407.07842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 10 Jul 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Conditional Positional Encodings for Vision Transformers](https://mlsanity.com/paper/2102.10882/) (2021)
- [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution](https://mlsanity.com/paper/2307.06304/) (2023)
- [Towards Robust Vision Transformer](https://mlsanity.com/paper/2105.07926/) (2021)
- [Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification](https://mlsanity.com/paper/2108.08728/) (2021)
- [A Discriminatively Learned CNN Embedding for Person Re-identification](https://mlsanity.com/paper/1611.05666/) (2016)
- [End-to-End Comparative Attention Networks for Person Re-identification](https://mlsanity.com/paper/1606.04404/) (2016)
- [VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification](https://mlsanity.com/paper/2004.06305/) (2020)
- [Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers](https://mlsanity.com/paper/2105.07402/) (2021)
- [Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems](https://mlsanity.com/paper/2110.07933/) (2021)
- [Trends in Vehicle Re-identification Past, Present, and Future: A Comprehensive Review](https://mlsanity.com/paper/2102.09744/) (2021)
- [Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task](https://mlsanity.com/paper/2204.02181/) (2022)
- [An Empirical Study of Vehicle Re-Identification on the AI City Challenge](https://mlsanity.com/paper/2105.09701/) (2021)
- [Improving Vehicle Re-Identification using CNN Latent Spaces: Metrics Comparison and Track-to-track Extension](https://mlsanity.com/paper/1910.09458/) (2019)

## Abstract (arXiv)

Vision Transformers (ViTs) have excelled in vehicle re-identification (ReID) tasks. However, non-square aspect ratios of image or video input might significantly affect the re-identification performance. To address this issue, we propose a novel ViT-based ReID framework in this paper, which fuses models trained on a variety of aspect ratios. Our main contributions are threefold: (i) We analyze aspect ratio performance on VeRi-776 and VehicleID datasets, guiding input settings based on aspect ratios of original images. (ii) We introduce patch-wise mixup intra-image during ViT patchification (guided by spatial attention scores) and implement uneven stride for better object aspect ratio matching. (iii) We propose a dynamic feature fusing ReID network, enhancing model robustness. Our ReID method achieves a significantly improved mean Average Precision (mAP) of 91.0% compared to the the closest state-of-the-art (CAL) result of 80.9% on VehicleID dataset.