Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification

TL;DR

This paper introduces a lightweight multi-branch deep learning architecture for vehicle re-identification that enhances feature diversity and discriminability while significantly reducing model size, outperforming state-of-the-art methods.

Contribution

Proposes a simple, efficient multi-branch architecture using grouped convolution and loss-branch-split strategies for improved vehicle re-identification performance.

Findings

Achieves 85.6% mAP on Veri-776 dataset.

Outperforms state-of-the-art in vehicle re-identification.

Uses 97% fewer parameters with metadata integration.

Abstract

This paper presents an efficient and lightweight multi-branch deep architecture to improve vehicle re-identification (V-ReID). While most V-ReID work uses a combination of complex multi-branch architectures to extract robust and diversified embeddings towards re-identification, we advocate that simple and lightweight architectures can be designed to fulfill the Re-ID task without compromising performance. We propose a combination of Grouped-convolution and Loss-Branch-Split strategies to design a multi-branch architecture that improve feature diversity and feature discriminability. We combine a ResNet50 global branch architecture with a BotNet self-attention branch architecture, both designed within a Loss-Branch-Split (LBS) strategy. We argue that specialized loss-branch-splitting helps to improve re-identification tasks by generating specialized re-identification features. A lightweight solution using grouped convolution is also proposed to mimic the learning of loss-splitting into multiple embeddings while significantly reducing the model size. In addition, we designed an improved solution to leverage additional metadata, such as camera ID and pose information, that uses 97% less parameters, further improving re-identification performance. In comparison to state-of-the-art (SoTA) methods, our approach outperforms competing solutions in Veri-776 by achieving 85.6% mAP and 97.7% CMC1 and obtains competitive results in Veri-Wild with 88.1% mAP and 96.3% CMC1. Overall, our work provides important insights into improving vehicle re-identification and presents a strong basis for other retrieval tasks. Our code is available at the https://github.com/videturfortuna/vehicle_reid_itsc2023.