8 citations · 12 across the 5 of their papers we have counts for
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cs.CV2025
Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification
Yinghao Jin, Xi Yang
Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under limited annotation budgets. Howe…
cs.CV2023★ 8 cited
SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
Zhiqing Xiao, Haobo Wang, Ying Jin +4
Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most…