# A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification

## TL;DR

This paper introduces a dual-path adaptive attention model for vehicle re-identification that effectively captures both global features and orientation-specific key-point features, achieving state-of-the-art results on VeRi-776.

## Contribution

The novel AAVER model combines global appearance and orientation-conditioned local features for improved vehicle re-identification.

## Key findings

- Achieves state-of-the-art accuracy on VeRi-776 dataset.
- Improves vehicle key-point prediction accuracy by over 7%.
- Effectively handles unconstrained vehicle re-identification scenarios.

## Abstract

In recent years, attention models have been extensively used for person and vehicle re-identification. Most re-identification methods are designed to focus attention on key-point locations. However, depending on the orientation, the contribution of each key-point varies. In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER). The global appearance path captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention on the most informative key-points. Through extensive experimentation, we show that the proposed AAVER method is able to accurately re-identify vehicles in unconstrained scenarios, yielding state of the art results on the challenging dataset VeRi-776. As a byproduct, the proposed system is also able to accurately predict vehicle key-points and shows an improvement of more than 7% over state of the art. The code for key-point estimation model is available at https://github.com/Pirazh/Vehicle_Key_Point_Orientation_Estimation.