Recently, a number of Few-Shot Fine-Grained Image Classification (FS-FGIC) methods have been proposed, but they primarily focus on better fine-grained feature extraction while overlooking two important issues. The first one is how to extract discriminative features for Fine-Grained Image Classification tasks while reducing trivial and non-generalizable sample level noise introduced in this procedure, to overcome the over-fitting problem under the setting of Few-Shot Learning. The second one is how to achieve satisfying feature matching between limited support and query samples with variable spatial positions and angles. To address these issues, we propose a novel Cross-layer and Cross-sample feature optimization Network for FS-FGIC, C2-Net for short. The proposed method consists of two main modules: Cross-Layer Feature Refinement (CLFR) module and Cross-Sample Feature Adjustment (CSFA) module. The CLFR module further refines the extracted features while integrating outputs from multiple layers to suppress sample-level feature noise interference. Additionally, the CSFA module addresses the feature mismatch between query and support samples through both channel activation and position matching operations. Extensive experiments have been conducted on five fine-grained benchmark datasets, and the results show that the C2-Net outperforms other state-of-the-art methods by a significant margin in most cases. Our code is available at: https://github.com/zenith0923/C2-Net.

The rapidly growing scale of data in practice poses demands on the efficiency of retrieval models. However, for fine-grained image retrieval task, there are inherent contradictions in the design of hashing based efficient models. Firstly, the limited information embedding capacity of low-dimensional binary hash codes, coupled with the detailed information required to describe fine-grained categories, results in a contradiction in feature learning. Secondly, there is also a contradiction between the complexity of fine-grained feature extraction models and retrieval efficiency. To address these issues, in this paper, we propose the characteristics matching based hash codes generation method. Coupled with the cross-layer semantic information transfer module and the multi-region feature embedding module, the proposed method can generate hash codes that effectively capture fine-grained differences among samples while ensuring efficient inference. Extensive experiments on widely used datasets demonstrate that our method can significantly outperform state-of-the-art methods.

In recent years, the Few-Shot Fine-Grained Image Classification (FS-FGIC) problem has gained widespread attention. A number of effective methods have been proposed that focus on extracting discriminative information within high-level features in a single episode/task. However, this is insufficient for addressing the cross-task challenges of FS-FGIC, which is represented in two aspects. On the one hand, from the perspective of the Fine-Grained Image Classification (FGIC) task, there is a need to supplement the model with mid-level features containing rich fine-grained information. On the other hand, from the perspective of the Few-Shot Learning (FSL) task, explicit modeling of cross-task general knowledge is required. In this paper, we propose a novel Bi-directional Task-Guided Network (BTG-Net) to tackle these issues. Specifically, from the FGIC task perspective, we design the Semantic-Guided Noise Filtering (SGNF) module to filter noise on mid-level features rich in detailed information. Further, from the FSL task perspective, the General Knowledge Prompt Modeling (GKPM) module is proposed to retain the cross-task general knowledge by utilizing the prompting mechanism, thereby enhancing the model's generalization performance on novel classes. We have conducted extensive experiments on five fine-grained benchmark datasets, and the results demonstrate that BTG-Net outperforms state-of-the-art methods comprehensively.

Data augmentation is a common technique to improve the generalization performance of models for image classification. Although methods such as Mixup and CutMix that mix images randomly are indeed instrumental in general image classification, randomly swapping or masking regions is not friendly to fine-grained images, since the key to fine-grained image classification precisely lies in discriminative and informative regions, and it is unreasonable to generate labels solely consistent with the proportion of synthesis. Some erasing methods like Cutout even endanger fine-grained image classification because of erasing the discriminative regions by chance. In this article, we propose the Same Category Same Semantics Mixing method (S3Mix) corresponding to the characteristics of fine-grained images. Specifically, we limit the mixture to regions of the same category and semantics. The core of the method is two constraints. The exchange with the semantic region ensures the discrimination and semantics integrity of the generated image, and the exchange in the same class avoids the problem of unreasonable label generation. At the same time, we propose a homology loss to promote the semantic relationship between the generated positive image pairs. Experiments have been conducted on four fine-grained datasets, and the results show the proposed method is superior to the traditional image augmentation methods as well as some fine-grained data augmentation methods.