Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn novel classes with limited samples after pre-training on a set of base classes. To avoid catastrophic forgetting and overfitting, most FSCIL methods first train the model on the base classes and then freeze the feature extractor in the incremental sessions. However, the reliance on nearest neighbor classification makes FSCIL prone to the hubness phenomenon, which negatively impacts performance in this dynamic and open scenario. While recent methods attempt to adapt to the dynamic and open nature of FSCIL, they are often limited to biased optimizations to the feature space. In this paper, we pioneer the theoretical analysis of the inherent hubness in FSCIL. To mitigate the negative effects of hubness, we propose a novel Attraction Diminishing and Distributing (D2A) method from the essential perspectives of distance metric and feature space. Extensive experimental results demonstrate that our method can broadly and significantly improve the performance of existing methods.

Recently proposed Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL) offers a practical and efficient solution for enabling models to incrementally learn new fine-grained categories under limited data conditions. However, existing methods still settle for the fine-grained feature extraction capabilities learned from the base classes. Unlike conventional datasets, fine-grained categories exhibit subtle inter-class variations, naturally fostering latent synergy among sub-categories. Meanwhile, the incremental learning framework offers an opportunity to progressively strengthen this synergy by incorporating new sub-category data over time. Motivated by this, we theoretically formulate the FSCIL problem and derive a generalization error bound within a shared fine-grained meta-category environment. Guided by our theoretical insights, we design a novel Meta-Environment Learner (MEL) for FG-FSCIL, which evolves fine-grained feature extraction to enhance meta-environment understanding and simultaneously regularizes hypothesis space complexity. Extensive experiments demonstrate that our method consistently and significantly outperforms existing approaches.

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.

Recent research on few-shot fine-grained image classification (FSFG) has predominantly focused on extracting discriminative features. The limited attention paid to the role of loss functions has resulted in weaker preservation of similarity relationships between query and support instances, thereby potentially limiting the performance of FSFG. In this regard, we analyze the limitations of widely adopted cross-entropy loss and introduce a novel Angular ISotonic (AIS) loss. The AIS loss introduces an angular margin to constrain the prototypes to maintain a certain distance from a pre-set threshold. It guides the model to converge more stably, learn clearer boundaries among highly similar classes, and achieve higher accuracy faster with limited instances. Moreover, to better accommodate the feature requirements of the AIS loss and fully exploit its potential in FSFG, we propose a Multi-Layer Integration (MLI) network that captures object features from multiple perspectives to provide more comprehensive and informative representations of the input images. Extensive experiments demonstrate the effectiveness of our proposed method on four standard fine-grained benchmarks. Codes are available at: https://github.com/Legenddddd/AIS-MLI.