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Zhen-Duo Chen

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6 papers

1 author row

TMLR Journal 2026 Journal Article

Hierarchical Filtering and Refinement Classification for Few-Shot Class-Incremental Learning

Li-Jun Zhao
Zhen-Duo Chen
Xin Luo
Xin-Shun Xu

Few-shot class-incremental learning (FSCIL) aims at recognizing novel classes continually with limited novel class samples. A mainstream baseline for FSCIL is first to train the whole model in the base session, then freeze the feature extractor in the incremental sessions. Despite achieving high overall accuracy, most methods exhibit notably low accuracy on incremental classes. While some recent methods have recognized this issue, their strategies remain constrained by a unified classification objective across all samples, making it difficult to simultaneously satisfy the performance requirements of both base and incremental classes. In this paper, considering that base and incremental classes play different yet both critical roles in FSCIL, we approach FSCIL from a more structured perspective by decomposing the overall classification objective into three sub-objectives. Building on this insight, we propose a novel classification framework called Hierarchical Filtering and Refinement Classification (HFRC) to hierarchically decompose and address the classification task. Extensive experiments demonstrate that our method effectively balances the classification accuracy between base and incremental classes, and achieves superior performance compared to state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

Evolving and Regularizing Meta-Environment Learner for Fine-Grained Few-Shot Class-Incremental Learning

Li-Jun Zhao
Zhen-Duo Chen
Yongxin Wang
Xin Luo
Xin-Shun Xu

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.

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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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AAAI Conference 2022 Conference Paper

Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data

Xiao-ming Wu
Xin Luo
Yu-Wei Zhan
Chen-Lu Ding
Zhen-Duo Chen
Xin-Shun Xu

With the vigorous development of multimedia equipments and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has drawn lots of attention in recent years, there still remain some problems. The first point is that existing methods are mainly designed in batch mode and not able to efficiently handle streaming multi-modal data. The second point is that all existing online multi-modal hashing methods fail to effectively handle unseen new classes which come continuously with streaming data chunks. In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). We design novel semantic-enhanced representation for data, which could help handle the new coming classes, and thereby construct the enhanced semantic objective function. An efficient and effective discrete online optimization algorithm is further proposed for OASIS. Extensive experiments show that our method can exceed the state-of-the-art models. For good reproducibility and benefiting the community, our code and data are already publicly available.

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AAAI Conference 2018 Conference Paper

Dual Deep Neural Networks Cross-Modal Hashing

Zhen-Duo Chen
Wan-Jin Yu
Chuan-Xiang Li
Liqiang Nie
Xin-Shun Xu

Recently, deep hashing methods have attracted much attention in multimedia retrieval task. Some of them can even perform cross-modal retrieval. However, almost all existing deep cross-modal hashing methods are pairwise optimizing methods, which means that they become time-consuming if they are extended to large scale datasets. In this paper, we propose a novel tri-stage deep cross-modal hashing method – Dual Deep Neural Networks Cross-Modal Hashing, i. e. , DDCMH, which employs two deep networks to generate hash codes for different modalities. Speciﬁcally, in Stage 1, it leverages a single-modal hashing method to generate the initial binary codes of textual modality of training samples; in Stage 2, these binary codes are treated as supervised information to train an image network, which maps visual modality to a binary representation; in Stage 3, the visual modality codes are reconstructed according to a reconstruction procedure, and used as supervised information to train a text network, which generates the binary codes for textual modality. By doing this, DDCMH can make full use of inter-modal information to obtain high quality binary codes, and avoid the problem of pairwise optimization by optimizing different modalities independently. The proposed method can be treated as a framework which can extend any single-modal hashing method to perform cross-modal search task. DDCMH is tested on several benchmark datasets. The results demonstrate that it outperforms both deep and shallow state-of-the-art hashing methods.

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