Image-based Vehicle Re-identification Model with Adaptive Attention Modules and Metadata Re-ranking

TL;DR

This paper introduces a vehicle re-identification model utilizing adaptive attention modules and metadata re-ranking, achieving improved accuracy with fewer annotations on challenging datasets.

Contribution

The paper presents a novel vehicle re-identification model that reduces annotation requirements and incorporates metadata-based re-ranking for enhanced performance.

Findings

Achieves 37.25% mAP on CVPR AI City Challenge 2020 dataset

Outperforms previous models with fewer label annotations

Demonstrates effectiveness of adaptive attention and metadata re-ranking

Abstract

Vehicle Re-identification is a challenging task due to intra-class variability and inter-class similarity across non-overlapping cameras. To tackle these problems, recently proposed methods require additional annotation to extract more features for false positive image exclusion. In this paper, we propose a model powered by adaptive attention modules that requires fewer label annotations but still out-performs the previous models. We also include a re-ranking method that takes account of the importance of metadata feature embeddings in our paper. The proposed method is evaluated on CVPR AI City Challenge 2020 dataset and achieves mAP of 37.25% in Track 2.