1 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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…
Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks
Anqi Zhao, Tong Chu, Yahao Liu +3
In this work, we study the black-box targeted attack problem from the model discrepancy perspective. On the theoretical side, we present a generalization error bound for black-box…
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…