Xingyu Feng — accepted AI-conference papers

2026

CoGenSAM: Codebook-Interactive Generative Labeling for Adapting SAM to Crack Segmentation

Zhuangzhuang Chen, Nuo Chen, Dachong Li, Zhiliang Lin, Xingyu Feng, Yifan Zhang +2

AAAI 2026technical

The goal of this work is to adapt Segment Anything Models (SAM) into crack segmentation tasks via automatic label generation, thus eliminating manual annotation cost. In this regard, an intuitive approach is to extract edges of crack samples and generate labels via the dilation and erosion processes

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2025

Attack-inspired Calibration Loss for Calibrating Crack Recognition

Zhuangzhuang Chen, Qiangyu Chen, Jiahao Zhang, Zhiliang Lin, Xingyu Feng, Jie Chen +1

AAAI 2025technical

Deep neural networks (DNNs) have substantially achieved high predictive accuracy in many vision tasks. However, we find that they are poorly calibrated for crack recognition tasks, as these DNNs tend to produce both under-confident and over-confident predictions in such safety-critical applications,…

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