76 citations · 94 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
LayerMatch: Do Pseudo-labels Benefit All Layers?
Chaoqi Liang, Guanglei Yang, Lifeng Qiao +4
Deep neural networks have achieved remarkable performance across various tasks when supplied with large-scale labeled data. However, the collection of labeled data can be time-cons…
cs.CV2023★ 17 cited
Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors
Yancheng Cai, Bo Zhang, Baopu Li +4
Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applica…
cs.CV2017★ 76 cited
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Hongliang Yan, Yukang Ding, Peihua Li +3
In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains. However, existing MMD-b…