Presents MMIO benchmark and RTVP method achieving state-of-the-art 42.2% AP in zero-shot industrial defect detection.
arXiv preprint arXiv:2306.13731 , year=
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Presents MMIOC-1M benchmark with 1M+ samples across 14 super-categories and RTVPNet with domain projection, sparse sampling, and bidirectional interaction, claiming SOTA on MMIOC-1M, LVIS, and COCO.
A2ONet improves robustness of liver surface landmark detection in laparoscopic surgery via illumination field compensation, frequency-orientation selective filtering, and alternating seg-curve optimization, with reported gains on three datasets.
CellNet applies regression-based deep learning to count cells from sparse point annotations in microscopy images and claims better performance than zero-shot methods in low-data regimes.
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
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Data-Centric Foundation Models in Computational Healthcare: A Survey
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.