Part-Guided Attention Learning for Vehicle Instance Retrieval

TL;DR

This paper introduces PGAN, a novel vehicle retrieval method combining part-guided bottom-up and top-down attention to improve discriminative feature learning, achieving state-of-the-art results on large datasets.

Contribution

The paper proposes a Part-Guided Attention Network that detects vehicle parts and salient regions, adaptively weights their importance, and integrates global and part features for enhanced retrieval accuracy.

Findings

Achieves new state-of-the-art performance on four large-scale datasets.

Effectively combines bottom-up and top-down attention mechanisms.

Improves discriminative feature learning for vehicle instance retrieval.

Abstract

Vehicle instance retrieval often requires one to recognize the fine-grained visual differences between vehicles. Besides the holistic appearance of vehicles which is easily affected by the viewpoint variation and distortion, vehicle parts also provide crucial cues to differentiate near-identical vehicles. Motivated by these observations, we introduce a Part-Guided Attention Network (PGAN) to pinpoint the prominent part regions and effectively combine the global and part information for discriminative feature learning. PGAN first detects the locations of different part components and salient regions regardless of the vehicle identity, which serve as the bottom-up attention to narrow down the possible searching regions. To estimate the importance of detected parts, we propose a Part Attention Module (PAM) to adaptively locate the most discriminative regions with high-attention weights and suppress the distraction of irrelevant parts with relatively low weights. The PAM is guided by the instance retrieval loss and therefore provides top-down attention that enables attention to be calculated at the level of car parts and other salient regions. Finally, we aggregate the global appearance and part features to improve the feature performance further. The PGAN combines part-guided bottom-up and top-down attention, global and part visual features in an end-to-end framework. Extensive experiments demonstrate that the proposed method achieves new state-of-the-art vehicle instance retrieval performance on four large-scale benchmark datasets.