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Haiming Yao

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation

Yuxin Jiang
Wei Luo
Hui Zhang
Qiyu Chen
Haiming Yao
Weiming Shen
Yunkang Cao

We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textual cues through a crossmodal prompt encoding scheme, Anomagic leverages rich contextual information to steer an inpainting‐based generation pipeline. A subsequent contrastive refinement strategy enforces precise alignment between synthesized anomalies and their masks, thereby bolstering downstream anomaly detection accuracy. To facilitate training, we introduce AnomVerse, a collection of 12,987 anomaly–mask–caption triplets assembled from 13 publicly available datasets, where captions are automatically generated by multimodal large language models using structured visual prompts and template‐based textual hints. Extensive experiments demonstrate that Anomagic trained on AnomVerse can synthesize more realistic and varied anomalies than prior methods, yielding superior improvements in downstream anomaly detection. Furthermore, Anomagic can generate anomalies for any normal‐category image using user‐defined prompts, establishing a versatile foundation model for anomaly generation.

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JBHI Journal 2026 Journal Article

Mining Global and Local Semantics From Unlabeled Spectra for Spectral Classification

Wei Luo
Haiming Yao
Ang Gao
Tao Zhou
Xue Wang

Non-destructive detection methods based on molecular vibrational spectroscopy are pivotal in fields such as analytical chemistry and medical diagnostics. Recent advances have integrated deep learning with vibrational spectroscopy, significantly enhancing spectral recognition accuracy. However, these methods often rely on large annotated spectral datasets, limiting their general applicability. To address this limitation, we propose a novel approach, G lobal and L ocal S emantics M ining (GLSM), which leverages self-supervised learning to capture the global and local semantic information of unlabeled spectra, obviating the need for extensive annotated data. We devise two proxy tasks: global semantic mining and local semantic mining. The global semantic mining task is based on the premise that different views of the same spectrum can be mutually transformed, enabling the model to capture domain-invariant features across various perspectives and thereby develop a global understanding of the spectral data. This, in turn, enhances the model’s robustness to variations in peak positions. Meanwhile, the local semantic mining task posits that noisy spectra can be reconstructed into noise-free spectra, thereby facilitating the extraction of local patterns and fine-grained details, such as subtle variations in peak intensities. By combining both self-supervised tasks, our model effectively captures the global and local semantic information of the spectrum. The pre-trained model can be fine-tuned with a limited amount of labeled homologous or heterologous spectral data for semi-supervised or transfer learning-based spectral classification. Extensive experiments on three datasets in semi-supervised and transfer learning-based spectral recognition tasks comprehensively validate the effectiveness of our GLSM method, demonstrating its significant potential for real-world spectral analysis applications.

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AAAI Conference 2026 Conference Paper

Parameter-, Memory-, Time-Efficient Multi-Task Dense Vision Adaptation

Haiming Yao
Wei Luo
Qiyu Chen
Jianxing Liao
Wei You

While adapting pretrained vision models to downstream dense prediction tasks is widely used, current methods often overlook adaptation efficiency, especially in the context of multi-task learning (MTL). Although parameter-efficient fine-tuning (PEFT) methods can enhance parameter efficiency, broader aspects such as GPU memory and training time efficiency remain underexplored. In this paper, we propose a new paradigm that simultaneously achieves efficiency in Parameters, GPU Memory, and Training Time for Multi-Task Dense Vision Adaptation. Specifically, we propose a dual-branch framework, in which a frozen pretrained backbone serves as the generic main branch, and the proposed Bi-Directional Task Adaptation (BDTA) modules are integrated in parallel to form a task bypass branch that extracts adaptation features required by multiple specific tasks. This adaptation module is lightweight, efficient, and does not require backpropagation through the large pre-trained backbone, thus avoiding resource-intensive gradient computations. Moreover, a Mixture of Task Experts mechanism (MoTE) is further proposed to integrate adaptation features across tasks and scales, thereby obtaining more robust representations tailored for dense prediction tasks. On the PASCAL-Context benchmark, our method achieves over 2× relative performance improvement compared to the best prior multi-task PEFT method, while using only ~30% of the parameters, ~50% of the memory, and ~60% of the training time, demonstrating superior overall adaptation efficiency.

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AAAI Conference 2026 Conference Paper

TDSS: Task Dynamic-Synergistic Skill Adaptation for Boosting Efficient and Scalable Multi-Task Learning in Dense Visual Prediction

Haiming Yao
Qiyu Chen
Wei Luo
Zheng Zhang
Jianxing Liao
Wei You

The transfer of knowledge from large-scale pre-trained models to diverse downstream tasks has achieved remarkable success. Beyond the traditional full fine-tuning paradigm, Parameter-Efficient Fine-Tuning (PEFT) has emerged as a more efficient model adaptation approach. However, applying existing PEFT methods to adapt dense vision models, particularly in multi-task settings, remains inadequately explored due to their low efficiency, limited task scalability, and neglect of cross-task fine-tuning interactions. To address these challenges, we propose the Task Dynamic-Synergistic Skill Adaptation, termed TDSS, an efficient and scalable multi-task model adaptation framework for dense visual predictions. TDSS comprises two key components: Task-Dynamic Skill Adapters (TDSA) and Task-Synergistic Adaptation Interaction (TSAI). Specifically, TDSA are inserted in parallel into pre-trained vision models to extract task-specific adapted features through the construction of skill representation experts and task dynamic gating. TSAI is developed to enhance cross-task adaptation interaction by bridging global generic and task-specific adapted features. Extensive experiments on multi-task dense visual predictions demonstrate that TDSS surpasses existing state-of-the-art parameter-efficient fine-tuning methods, while exhibiting remarkable efficiency and scalability in parameters and computational complexity.

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EAAI Journal 2025 Journal Article

Adversarial contrastive domain-generative learning for bacteria Raman spectrum joint denoising and cross-domain identification

Haiming Yao
Wei Luo
Xue Wang

Raman spectroscopy, as a label-free detection technology, has been widely utilized in the clinical diagnosis of pathogenic bacteria. However, Raman signals are naturally weak and sensitive to the condition of the acquisition process. The characteristic spectra of a bacteria can manifest varying signal-to-noise ratios and domain discrepancies under different acquisition conditions. Consequently, existing methods often face challenges when identifying unobserved acquisition conditions, i. e. , the testing acquisition conditions are unavailable during model training. In this article, a generic framework, namely, an adversarial contrastive domain-generative learning framework, is proposed for joint Raman spectroscopy denoising and cross-domain identification. The proposed method is composed of a domain generation module and a domain task module. Through adversarial learning between these two modules, it utilizes only a single available source domain spectral data to generate extended denoised domains that are semantically consistent with the source domain and extract domain-invariant representations. Experimental results show that the proposed method significantly enhances diagnostic performance, with an average recognition accuracy improvement of over +5. 0% under unknown acquisition conditions compared to existing methods. Notably, the proposed method also performs simultaneous denoising of the spectra, enhancing the signal-to-noise ratio of the original signal by an average gain of +3. 0, without requiring noise-free ground truth. These results suggest that the proposed method holds great potential as a diagnostic tool for real-world clinical cases.

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EAAI Journal 2024 Journal Article

Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection

Haiming Yao
Wei Luo
Weihang Zhang
Xiaotian Zhang
Zhenfeng Qiang
Donghao Luo

The unsupervised detection and localization of image anomalies hold significant importance across various domains, particularly in industrial quality inspection. Despite its widespread utilization, this task remains inherently challenging due to its reliance solely on defect-free normal knowledge. This paper presents the local–global normality learning and discrepancy normalizing flow, a new state-of-the-art model for unsupervised image anomaly detection and localization. In contrast to existing methods, It adopts a two-stream approach that considers both local and global semantics, ensuring stable detection of abnormalities. The framework comprises two key components: the dual-branch Transformer and the discrepancy normalizing flow, facilitating reconstruction and discrimination. The proposed framework leverages pre-trained convolutional neural networks to extract multi-scale feature embeddings, followed by a novel dual-branch transformer that achieves feature reconstruction from local and global perspectives. The local reconstruction employs self-attention, while the global reconstruction incorporates global prototype tokens and semantic query tokens by the aggregation-cross attention mechanism. Moreover, discrepancy normalizing flow is developed to estimate the likelihood of anomalies based on the discrepancy between pre-trained features and local/global reconstruction results. Extensive validation on established public benchmarks confirms that our method achieves state-of-the-art performance with the proposed local–global reconstruction and discrimination dual-stream framework.

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EAAI Journal 2024 Journal Article

Template-based Feature Aggregation Network for industrial anomaly detection

Wei Luo
Haiming Yao
Wenyong Yu

Industrial anomaly detection plays a crucial role in ensuring product quality control. Therefore, proposing an effective anomaly detection model is of great significance. While existing feature-reconstruction methods have demonstrated excellent performance, they face challenges with shortcut learning, which can lead to undesirable reconstruction of anomalous features. To address this concern, we present a novel feature-reconstruction model called the Template-based Feature Aggregation Network (TFA-Net) for anomaly detection via template-based feature aggregation. Specifically, TFA-Net first extracts multiple hierarchical features from a pre-trained convolutional neural network for a fixed template image and an input image. Instead of directly reconstructing input features, TFA-Net aggregates them onto the template features, effectively filtering out anomalous features that exhibit low similarity to normal template features. Next, TFA-Net utilizes the template features that have already fused normal features in the input features to refine feature details and obtain the reconstructed feature map. Finally, the defective regions can be located by comparing the differences between the input and reconstructed features. Our template-based feature aggregation schema yields a nontrivial and meaningful feature reconstruction task. The simple, yet efficient, TFA-Net exhibits state-of-the-art detection performance on various real-world industrial datasets. Additionally, it fulfills the real-time demands of industrial scenarios, rendering it highly suitable for practical applications in the industry.

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