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cs.CV2025★ 1 cited
Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples
Weiwei Li, Junzhuo Liu, Yuanyuan Ren +3
Deep learning models are known to often learn features that spuriously correlate with the class label during training but are irrelevant to the prediction task. Existing methods ty…
cs.CV2025
Balanced Sharpness-Aware Minimization for Imbalanced Regression
Yahao Liu, Qin Wang, Lixin Duan +1
Regression is fundamental in computer vision and is widely used in various tasks including age estimation, depth estimation, target localization, \etc However, real-world data ofte…
cs.CV2022
Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation
Yahao Liu, Jinhong Deng, Jiale Tao +3
Existing works typically treat cross-domain semantic segmentation (CDSS) as a data distribution mismatch problem and focus on aligning the marginal distribution or conditional dist…