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SePArate, a weakly supervised wafer defect segmentation method, enables pixel-level separation of patterns by leveraging only image-level annotations and outperforms the baselines.
STARLINC, the first ML-based framework for satellite trail removal without requiring tedious pixel-level annotation of astronomical images, is introduced, establishing STARLINC as a scalable solution for next-generation astronomical surveys.
TopGQ, an accurate post-training GNN quantization framework, alleviating redundant quantization overhead, is presented, and dual-axis scale absorption is proposed, which enables activation quantization along both the outer and inner dimensions by merging one into the adjacency matrix.
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