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Wenbin Qian

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AAAI Conference 2025 Conference Paper

GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning

Jintao Huang
Yiu-ming Cheung
Chi-man Vong
Wenbin Qian

Partial label learning (PLL) is a complicated weakly supervised multi-classification task compounded by class imbalance. Currently, existing methods only rely on inter-class pseudo-labeling from inter-class features, often overlooking the significant impact of the intra-class imbalanced features combined with the inter-class. To address these limitations, we introduce Granular Ball Representation for Imbalanced PLL (GBRIP), a novel framework for imbalanced PLL. GBRIP utilizes coarse-grained granular ball representation and multi-center loss to construct a granular ball-based feature space through unsupervised learning, effectively capturing the feature distribution within each class. GBRIP mitigates the impact of confusing features by systematically refining label disambiguation and estimating imbalance distributions. The novel multi-center loss function enhances learning by emphasizing the relationships between samples and their respective centers within the granular balls. Extensive experiments on standard benchmarks demonstrate that GBRIP outperforms existing state-of-the-art methods, offering a robust solution to the challenges of imbalanced PLL.

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EAAI Journal 2024 Journal Article

Label correlations-based multi-label feature selection with label enhancement

Wenbin Qian
Yinsong Xiong
Weiping Ding
Jintao Huang
Chi-man Vong

Feature selection, as an important pre-processing technique, can efficiently mitigate the issue of “the curse of dimensionality” by selecting discriminative features especially for multi-label learning, a discriminative feature subset can improve the classification accuracy. The existing feature selection methods for multi-label classification address the problem of label ambiguity by with logical labels. However, the significance of each label is often different in many practical applications. Using logical label to train the model may result in unsatisfactory performance due to not considering the importance of related labels with each sample. To address this issue, a novel multi-label feature selection algorithm is proposed with two-step: label enhancement and label correlations-based feature selection with label enhancement. In the step of label enhancement, a framework of label enhancement based on deep forest is utilized to transform the logical label to label distribution, which contains rich semantic information and then guides a more correct exploration of semantic correlations. In the step of feature selection, a novel multi-label feature selection algorithm is proposed based on label distribution data. Firstly, the samples are divided into multiple different clusters by using spectral clustering in the label space. Then, the label correlations can be reflected by multiple different clusters. Finally, the l 2, 1 -norm is used to construct an objective function to achieve multi-label feature selection. Experimental results demonstrate that competitiveness of the proposed algorithm over six state-of-the-art multi-label feature selection algorithms on eighteen benchmark datasets in terms of six widely accepted evaluation metrics.

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AAAI Conference 2024 Conference Paper

Weakly-Supervised Mirror Detection via Scribble Annotations

Mingfeng Zha
Yunqiang Pei
Guoqing Wang
Tianyu Li
Yang Yang
Wenbin Qian
Heng Tao Shen

Mirror detection is of great significance for avoiding false recognition of reflected objects in computer vision tasks. Existing mirror detection frameworks usually follow a supervised setting, which relies heavily on high quality labels and suffers from poor generalization. To resolve this, we instead propose the first weakly-supervised mirror detection framework and also provide the first scribble-based mirror dataset. Specifically, we relabel 10,158 images, most of which have a labeled pixel ratio of less than 0.01 and take only about 8 seconds to label. Considering that the mirror regions usually show great scale variation, and also irregular and occluded, thus leading to issues of incomplete or over detection, we propose a local-global feature enhancement (LGFE) module to fully capture the context and details. Moreover, it is difficult to obtain basic mirror structure using scribble annotation, and the distinction between foreground (mirror) and background (non-mirror) features is not emphasized caused by mirror reflections. Therefore, we propose a foreground-aware mask attention (FAMA), integrating mirror edges and semantic features to complete mirror regions and suppressing the influence of backgrounds. Finally, to improve the robustness of the network, we propose a prototype contrast loss (PCL) to learn more general foreground features across images. Extensive experiments show that our network outperforms relevant state-of-the-art weakly supervised methods, and even some fully supervised methods. The dataset and codes are available at https://github.com/winter-flow/WSMD.

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