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Chunming Li

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3 papers

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AAAI Conference 2026 Conference Paper

Learning a Fix and Explore Framework for Continuous Generalized Category Discovery

Chunming Li
Shidong Wang
Haofeng Zhang

To address the limitations of transductive learning in evolving real-world scenarios where unknown categories may continuously emerge, Continual Generalized Category Discovery (C-GCD) presents a novel paradigm that extends conventional category discovery frameworks. Unlike traditional static learning environments, C-GCD requires models to incrementally discover novel categories across multiple operational phases while maintaining discrimination capabilities for previously learned classes, posing significant challenges in balancing stability and plasticity. Prior approaches typically employ parameter-level knowledge distillation from historical models to alleviate catastrophic forgetting, which effectively preserves prior knowledge and optimizes computational efficiency. However, our analysis reveals that the persistent availability of samples from previous stages enables more sophisticated knowledge preservation strategies. Specifically, we present a Fix and Explore strategy that employs distinct learning methodologies for different types of potential data, aiming to preserve the features of old categories as much as possible and gradually exploring the potential distribution of new class latent spaces, we can enhance the model's ability to discover novel categories. This paper investigates this effect and introduces a novel heuristic paradigm to solve the C-GCD problem, called Fix and Explore (FaE), which aims to provide sufficient imaginative space for new classes while preserving the classification ability for old tasks. We conducted experiments across multiple datasets and performed detailed comparisons. The results demonstrate that our method achieves state-of-the-art performance at each stage across all datasets.

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JBHI Journal 2025 Journal Article

DPPAT: Dual-Level Periodic Pattern-Aware Transformer for Heart Sound Murmur Identification

Zilan Hong
Wei Yu
Chunming Li
Botao Yang
Zehao Fan
Runguo Wei
Shengxian Tu

Developing heart sound classification algorithms for murmur identification is critical for early screening of heart diseases. However, identifying murmurs in long-duration heart sound signals can be challenging due to their weak features and interference from noise. Considering the periodic patterns of heart sounds and murmurs, periodic priors can be introduced to enhance murmur identification, an approach that remains underutilized in current methods. In this study, we propose a novel Dual-level Periodic Pattern-Aware Transformer (DPPAT) to implicitly leverage the periodic priors of heart sound signals without requiring cycle segmentation. In the regional-level, an Adaptive Period-Aligned Window Selection algorithm is designed for the model to extract periodic components while suppressing random noise using a Periodic Pattern Attention module. In the global-level, the model further integrates these periodic features in global-modeling to enhance the identification of murmur-discriminative features. Validated on the dataset from 2022 George B. Moody PhysioNet Challenge, our proposed method achieves a weighted accuracy of 84. 27% and an F1-score of 70. 38% through 10-fold cross-validation. The generalizability of DPPAT is further verified on two additional public datasets, including both heart sound and respiratory sound signals. Furthermore, attention visualizations provide a clear understanding of the focus of the model, highlighting the decision-making basis for murmur identification.

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IJCAI Conference 2025 Conference Paper

Few-shot Novel Category Discovery

Chunming Li
Shidong Wang
Haofeng Zhang

The recently proposed Novel Category Discovery (NCD) adapt paradigm of transductive learning hinders its application in more real-world scenarios. In fact, few labeled data in part of new categories can well alleviate this burden, which coincides with the ease that people can label few of new category data. Therefore, this paper presents a new setting in which a trained agent is able to flexibly switch between the tasks of identifying examples of known (labelled) classes and clustering novel (completely unlabeled) classes as the number of query examples increases by leveraging knowledge learned from only a few (handful) support examples. Drawing inspiration from the discovery of novel categories using prior-based clustering algorithms, we introduce a novel framework that further relaxes its assumptions to the real-world open set level by unifying the concept of model adaptability in few-shot learning. We refer to this setting as Few-Shot Novel Category Discovery (FSNCD) and propose Semi-supervised Hierarchical Clustering (SHC) and Uncertainty-aware K-means Clustering (UKC) to examine the model's reasoning capabilities. Extensive experiments and detailed analysis on five commonly used datasets demonstrate that our methods can achieve leading performance levels across different task settings and scenarios. Code is available at: https: //github. com/Ashengl/FSNCD.

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