A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval

TL;DR

This paper introduces FCAENet, a novel network for fine-grained image retrieval that enhances feature robustness and consistency using adaptive region erasing and pair-wise similarity loss, achieving state-of-the-art results.

Contribution

The paper proposes a feature consistency driven attention erasing network with adaptive augmentation and similarity loss for improved fine-grained image retrieval.

Findings

FCAENet outperforms existing methods on five benchmark datasets.

The adaptive region erasing improves robustness to subtle differences.

The similarity loss stabilizes relation between query and database hash codes.

Abstract

Large-scale fine-grained image retrieval has two main problems. First, low dimensional feature embedding can fasten the retrieval process but bring accuracy reduce due to overlooking the feature of significant attention regions of images in fine-grained datasets. Second, fine-grained images lead to the same category query hash codes mapping into the different cluster in database hash latent space. To handle these two issues, we propose a feature consistency driven attention erasing network (FCAENet) for fine-grained image retrieval. For the first issue, we propose an adaptive augmentation module in FCAENet, which is selective region erasing module (SREM). SREM makes the network more robust on subtle differences of fine-grained task by adaptively covering some regions of raw images. The feature extractor and hash layer can learn more representative hash code for fine-grained images by SREM. With regard to the second issue, we fully exploit the pair-wise similarity information and add the enhancing space relation loss (ESRL) in FCAENet to make the vulnerable relation stabler between the query hash code and database hash code. We conduct extensive experiments on five fine-grained benchmark datasets (CUB2011, Aircraft, NABirds, VegFru, Food101) for 12bits, 24bits, 32bits, 48bits hash code. The results show that FCAENet achieves the state-of-the-art (SOTA) fine-grained retrieval performance compared with other methods.