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Ruofan Wang

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

CADiff: Context-Aware Diffusion for Controllable Anomaly Generation in Anomaly Detection

Xuan Tong
Yuxuan Lin
Junxiong Lin
Xinji Mai
Haoran Wang
Zeng Tao
Yang Yao
Ruofan Wang

Generating anomalies is a crucial method to enhance detection and classification performance by expanding anomalous data repository. However, existing anomaly generation methods overlook the intrinsic entanglement between diverse anomaly types and product structures, leading to semantic ambiguity. We propose CADiff, a context-aware generation framework that reframes anomalies as compositional perturbations. Firstly, we propose Context-aware Text Prompt (CTP), a mechanism which contains multiple tokens that characterize anomalies and products separately to enhance the contextual consistency of generated images and refine the local variability of anomalies. Secondly, we develop Self-adaptive Spatial Control (SSC), a self-adaptive interaction design that mitigates anomaly leakage or missing phenomena. Thirdly, we introduce Intensity-controllable Attention Re-weighting (IAR), an inference scheduling scheme with the ability to amplify or attenuate abnormal semantic effects to improve generation diversity. Extensive experiments on MVTec AD and VisA datasets demonstrate the superiority of our proposed method over state-of-the-art methods in both realism and diversity of the generated results, and significantly improve the performance of downstream tasks, including anomaly detection, anomaly localization, and anomaly classification tasks.

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JBHI Journal 2026 Journal Article

MBE-UNet: Multi-Branch Boundary Enhanced U-Net for Ultrasound Segmentation

Qing Qin
Ziwei Lin
Guangyuan Gao
Chunxiao Han
Ruofan Wang
Yingmei Qin
Shanshan Li
Shan An

Accurately capturing object areas in medical images is crucial for the clinical diagnosis and treatment of diseases. Due to the inherent low contrast and blurry edges in ultrasound images, most existing CNN-based methods often yield unsatisfactory segmentation results, making ultrasound image segmentation a challenging task. This paper introduces a novel multi-branch boundary enhanced network (MBE-UNet) for automatic ultrasound image segmentation. This method can accurately segment targets and delineate boundaries simultaneously using a multi-branch network. First, a global pyramid attention module (GPAM) is designed to capture multi-scale contextual information. Second, we embed a boundary cascade module (BCM) in the main branch to ensure the network focuses on edge information flow and generates relatively desirable boundaries. Finally, a boundary feature fusion module (BFM) is used to integrate boundary and region information, obtaining a boundary enhanced region map. The visual results and quantitative analysis demonstrate that the proposed MBE-UNet outperforms classical segmentation networks on three publicly available ultrasound datasets.

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AAAI Conference 2024 Conference Paper

Towards Evidential and Class Separable Open Set Object Detection

Ruofan Wang
Rui-Wei Zhao
Xiaobo Zhang
Rui Feng

Detecting in open-world scenarios poses a formidable challenge for models intended for real-world deployment. The advanced closed set object detectors achieve impressive performance under the closed set setting, but often produce overconfident misprediction on unknown objects due to the lack of supervision. In this paper, we propose a novel Evidential Object Detector (EOD) to formulate the Open Set Object Detection (OSOD) problem from the perspective of Evidential Deep Learning (EDL) theory, which quantifies classification uncertainty by placing the Dirichlet Prior over the categorical distribution parameters. The task-specific customized evidential framework, equipped with meticulously designed model architecture and loss function, effectively bridges the gap between EDL theory and detection tasks. Moreover, we utilize contrastive learning as an implicit means of evidential regularization and to encourage the class separation in the latent space. Alongside, we innovatively model the background uncertainty to further improve the unknown discovery ability. Extensive experiments on benchmark datasets demonstrate the outperformance of the proposed method over existing ones.

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ECAI Conference 2023 Conference Paper

Towards Legal Judgment Summarization: A Structure-Enhanced Approach

Qiqi Wang 0005
Ruofan Wang
Kaiqi Zhao 0001
Robert Amor
Benjamin Liu
Xianda Zheng
Zeyu Zhang 0004
Zijian Huang 0003

Judgment summaries are beneficial for legal practitioners to comprehend and retrieve case law efficiently. Unlike summaries in general domains, e. g. , news, judgment summaries often require a clear structure. Such a structure helps readers grasp the information contained in the summary and reduces information loss. To the best of our knowledge, none of the existing text summarizers can generate summaries aligned with the summary structure in the legal domain. Inspired by this observation, this paper introduces a Summary Structure-Enhanced (SSE) method to synthesize structured summaries for legal documents. SSE can easily be incorporated into the Encoder-Decoder framework, which is commonly adopted in state-of-the-art text summarizers. Experiments on the datasets of New Zealand and Chinese judgments show that the proposed method consistently improves the performance of state-of-the-art summarizers in terms of Rouge scores.

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