Existing AI-generated image (AIGI) detectors perform well in-domain but degrade severely under distribution shift. We observe that this failure is mainly caused by content shortcuts, where detectors spuriously couple forgery artifacts with semantic content, such as object categories or demographic attributes, learning...
Xin-Yu Wu, Dong Li, Minglai Shao et al.· Proceedings of the Thirty-Fi...· 0 citations
Identifying morphologically distinct cell populations in time-lapse fluorescence microscopy is central to understanding how complex tissues develop and remodel, yet remains labor-intensive and subject to observer bias despite advances in cell segmentation. We present a generalizable computational framework for automate...
Prateek Verma, Chloe A. Kuebler, Minh-Hao Van et al.· IEEE/ACM International Confe...· 0 citations
Spatial omics (SO) technologies enable spatially resolved molecular profiling, while hematoxylin and eosin (H&E) imaging remains the gold standard for morphological assessment in clinical pathology. Recent computational advances increasingly place H&E images at the center of SO analysis, bridging morphology with transc...
Ning-Hui Hao, Boshen Yan, Dong Li et al.· Proceedings of the 32nd ACM...· 0 citations
A message extrapolation mechanism under soft uncertainty constraints is proposed to obtain the diverse counterfactual message distributions and a novel robust representation learning framework for dynamic graph domain generalization, LEMD is proposed.
Xiaoran Wei, Chen Zhao, Minglai Shao et al.· 0 citations
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