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Bin Luo

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Jul 2026

Visual–Label Alignment and Attribute-Aware Prompt for Multi-Label Image Recognition with Partial Labels

The problem of Multi-Label Image Recognition with Partial Labels (MLIR-PL) is a significant challenge in computer vision, primarily due to the scarcity and high cost of complete annotations. Recent advances have leveraged large-scale vision-language models, such as CLIP, to establish rich correspondences between images and their labels, thereby improving the MLIR-PL performance. However, the existing CLIP-based methods have not fully exploited fine-grained local image features to mitigate interference from semantically irrelevant regions. Moreover, many studies have oversimplified the use of prompt contexts, limiting their ability to comprehensively capture the multi-dimensional attributes of categories. To address these limitations, this article proposes a novel MLIR-PL model with Visual–Label Alignment and Attribute-Aware Prompt (VA \({}^{3}\) P), which sufficiently harnesses the capabilities of large-scale pre-trained vision-language models. In the model, we design a Visual–Label Alignment module to establish a mapping between local image features and category text representations, conspicuously reducing the interference from irrelevant regions. Additionally, our Attribute-Aware Prompt module offers diverse contextual information, providing a more comprehensive representation of the category’s attributes. Extensive experimental results on the COCO 2014 and VOC 2007 datasets, compared with multiple state-of-the-art methods, demonstrate that our model achieves the best performance comprehensively, verifying the advantages of the proposed model in the MLIR-PL task.

Dengdi Sun, Hongxing Xie, Zhenbin Cai et al. · 0 citations
Open access Aug 2026

Bidirectional uncertainty-aware region learning for semi-supervised medical image segmentation.

In semi-supervised medical image segmentation, the poor quality of unlabeled data and the uncertainty in the model's predictions often lead to the generation of incorrect pseudo-labels by the model. These errors accumulate throughout model training, thereby weakening the model's performance. We found that these erroneous pseudo-labels are typically concentrated in high-uncertainty regions. Traditional methods improve performance by directly discarding pseudo-labels in these regions, which can also result in neglecting potentially valuable training data. To alleviate this problem, we propose a bidirectional uncertainty-aware region learning strategy to fully utilize the precise supervision provided by labeled data and stabilize the training of unlabeled data. Specifically, in the training labeled data, we focus on high-uncertainty regions, using precise label information to guide the model's learning in potentially uncontrollable areas. Meanwhile, in the training of unlabeled data, we concentrate on low-uncertainty regions to reduce the interference of erroneous pseudo-labels on the model. Through this bidirectional learning strategy, the model's overall performance has significantly improved. Extensive experiments show that our proposed method achieves significant performance improvement on different medical image segmentation tasks.

Shiwei Zhou, Xin Liu, Haifeng Zhao et al. · 0 citations