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Yang Long

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

vMFCoOp: Towards Equilibrium on a Unified Hyperspherical Manifold for Prompting Biomedical VLMs

Minye Shao
Sihan Guo
Xinrun Li
Xingyu Miao
Haoran Duan
Yang Long

Recent advances in context optimization (CoOp) guided by large language model (LLM)–distilled medical semantic priors offer a scalable alternative to manual prompt engineering and full fine-tuning for adapting biomedical CLIP-based vision-language models (VLMs). However, prompt learning in this context is challenged by semantic misalignment between LLMs and CLIP variants due to divergent training corpora and model architectures; it further lacks scalability across continuously evolving families of foundation models. More critically, pairwise multimodal alignment via conventional Euclidean-space optimization lacks the capacity to model unified representations or apply localized geometric constraints, which tends to amplify modality gaps in complex biomedical imaging and destabilize few-shot adaptation. To address these challenges, we propose vMFCoOp, a framework that inversely estimates von Mises–Fisher (vMF) distributions on a shared Hyperspherical Manifold, aligning semantic biases between arbitrary LLMs and CLIP backbones via Unified Semantic Anchors to achieve robust biomedical prompting and superior few-shot classification. Grounded in three complementary constraints, vMFCoOp demonstrates consistent improvements across 14 medical datasets, 12 medical imaging modalities, and 13 anatomical regions, outperforming state-of-the-art methods in accuracy, generalization, and clinical applicability.

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EAAI Journal 2025 Journal Article

Synthesizing Spreading-out features for generative zero-shot image classification

Jingren Liu
Ke Sun
Zheng Zhang
Yang Long
Wankou Yang
Yunyang Yan
Haofeng Zhang

Suffering from the singularity of generated samples, the most of the existing generative models cannot achieve satisfactory performance on Generalized Zero-shot Learning tasks. Generative methods applied to generalized zero-shot learning mainly focus on the data generation of the entire class domain, and do not pay enough attention to the intra-instance relationships, which severely limits the robustness of generators. Therefore, we propose a model that mainly utilizes Cramer distance and Wasserstein Generative Adversarial Network -divergence to generate various diversified visual features, which effectively alleviate the domain shift problem and facilitates better classification than traditional Wasserstein distance and its variants. In addition, we also use perturbation-based attack strategy to standardize the range of generated features, so as not to generate too grotesque features and lead to incorrect classification results. Finally, supported by the above, we utilize two different classifiers to obtain better Generative Zero-shot Learning performance, i. e. , the traditional softmax classifier and the normalized prototypes classifier. Extensive experimental results show that our proposed method can outperform the most of state-of-the-art methods on five benchmark datasets in generalized zero-shot learning setting and various measurement criteria.

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JBHI Journal 2024 Journal Article

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning

Feiyang Yang
Xiongfei Li
Haoran Duan
Feilong Xu
Yawen Huang
Xiaoli Zhang
Yang Long
Yefeng Zheng

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations across different slices. This presents a significant hurdle to medical image segmentation, as conventional segmentation methods may either overlook the minority class or overly emphasize the majority class, ultimately leading to a decrease in the overall generalization ability of the segmentation results. To overcome this, we propose a novel approach based on multi-step reinforcement learning, which integrates prior knowledge of medical images and pixel-wise segmentation difficulty into the reward function. Our method treats each pixel as an individual agent, utilizing diverse actions to evaluate its relevance for segmentation. To validate the effectiveness of our approach, we conduct experiments on four imbalanced medical datasets, and the results show that our approach surpasses other state-of-the-art methods in highly imbalanced scenarios. These findings hold substantial implications for clinical diagnosis and research.

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AAAI Conference 2019 Conference Paper

Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding

Yan Huang
Yang Long
Liang Wang

Although image and sentence matching has been widely studied, its intrinsic few-shot problem is commonly ignored, which has become a bottleneck for further performance improvement. In this work, we focus on this challenging problem of few-shot image and sentence matching, and propose a Gated Visual-Semantic Embedding (GVSE) model to deal with it. The model consists of three corporative modules in terms of uncommon VSE, common VSE, and gated metric fusion. The uncommon VSE exploits external auxiliary resources to extract generic features for representing uncommon instances and words in images and sentences, and then integrates them by modeling their semantic relation to obtain global representations for association analysis. To better model other common instances and words in rest content of images and sentences, the common VSE learns their discriminative representations directly from scratch. After obtaining two similarity metrics from the two VSE modules with different advantages, the gated metric fusion module adaptively fuses them by automatically balancing their relative importance. Based on the fused metric, we perform extensive experiments in terms of few-shot and conventional image and sentence matching, and demonstrate the effectiveness of the proposed model by achieving the state-of-the-art results on two public benchmark datasets.

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AAAI Conference 2018 Conference Paper

Towards Affordable Semantic Searching: Zero-Shot Retrieval via Dominant Attributes

Yang Long
Li Liu
Yuming Shen
Ling Shao

Instance-level retrieval has become an essential paradigm to index and retrieves images from large-scale databases. Conventional instance search requires at least an example of the query image to retrieve images that contain the same object instance. Existing semantic retrieval can only search semantically-related images, such as those sharing the same category or a set of tags, not the exact instances. Meanwhile, the unrealistic assumption is that all categories or tags are known beforehand. Training models for these semantic concepts highly rely on instance-level attributes or human captions which are expensive to acquire. Given the above challenges, this paper studies the Zero-shot Retrieval problem that aims for instance-level image search using only a few dominant attributes. The contributions are: 1) we utilise automatic word embedding to infer class-level attributes to circumvent expensive human labelling; 2) the inferred class-attributes can be extended into discriminative instance attributes through our proposed Latent Instance Attributes Discovery (LIAD) algorithm; 3) our method is not restricted to complete attribute signatures, query of dominant attributes can also be dealt with. On two benchmarks, CUB and SUN, extensive experiments demonstrate that our method can achieve promising performance for the problem. Moreover, our approach can also beneﬁt conventional ZSL tasks.

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