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Zhen-Xiang Ma

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2 papers

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AAAI Conference 2025 Conference Paper

Few-Shot Fine-Grained Image Classification with Progressively Feature Refinement and Continuous Relationship Modeling

Zhen-Xiang Ma
Zhen-Duo Chen
Tai Zheng
Xin Luo
Zixia Jia
Xin-Shun Xu

Recently, a number of effective methods have been proposed to tackle the challenging task of Few-Shot Fine-Grained Image Classification (FS-FGIC). However, how to fully leverage the backbone network to discover and extract detailed features to generate more discriminative class prototypes, as well as how to accurately model the similarity relationship between query samples and the class prototypes, are still issues to be further considered. Therefore, we propose a novel progreSsively featUre refInement and conTinuous rElationship moDeling method, SUITED for short, to address these two issues existing in the State-of-the-Art FS-FGIC methods. Specifically, we design the Progressive Feature Refinement Module (PFRM) to fully exploit the backbone network's progressive feature extraction capabilities, forming multi-scale feature representations to further enhance discriminative features. Then, the Continuous Relationship Modeling Module (CRMM) is proposed to capture the dependencies between query samples and the corresponding class prototypes, achieving precise optimization of the distances among corresponding sample points in the feature space. We conducted extensive experiments on five fine-grained benchmark datasets, and the experimental results demonstrate that the proposed method is comprehensively ahead of the existing State-of-the-Art methods.

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AAAI Conference 2024 Conference Paper

Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification

Zhen-Xiang Ma
Zhen-Duo Chen
Li-Jun Zhao
Zi-Chao Zhang
Xin Luo
Xin-Shun Xu

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.

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