Fine-grained image retrieval (FGIR) is to learn visual representations that
distinguish visually similar objects while maintaining generalization. Existing
methods propose to generate discriminative features, but rarely consider the
particularity of the FGIR task itself. This paper presents a meticulous
analysis leading to the proposal of practical guidelines to identify
subcategory-specific discrepancies and generate discriminative features to
design effective FGIR models. These guidelines include emphasizing the object
(G1), highlighting subcategory-specific discrepancies (G2), and employing
effective training strategy (G3). Following G1 and G2, we design a novel Dual
Visual Filtering mechanism for the plain visual transformer, denoted as DVF, to
capture subcategory-specific discrepancies. Specifically, the dual visual
filtering mechanism comprises an object-oriented module and a semantic-oriented
module. These components serve to magnify objects and identify discriminative
regions, respectively. Following G3, we implement a discriminative model
training strategy to improve the discriminability and generalization ability of
DVF. Extensive analysis and ablation studies confirm the efficacy of our
proposed guidelines. Without bells and whistles, the proposed DVF achieves
state-of-the-art performance on three widely-used fine-grained datasets in
closed-set and open-set settings.

本文提出了实用的细粒度图像检索模型设计指南，包括强调对象、凸显子类别特异性差异以及采用有效的训练策略。通过遵循指南并设计了一种新颖的双重视觉过滤机制（DVF），本文在三个广泛使用的细粒度数据集上实现了最先进的性能表现。