1 citations · 1 across the 2 of their papers we have counts for
2 papers
eess.IV2023★ 1 cited
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
Li Lin, Jiewei Wu, Yixiang Liu +2
Federated learning (FL) enables multiple sites to collaboratively train powerful deep models without compromising data privacy and security. The statistical heterogeneity (e.g., no…
cs.CV2023
GlocalFuse-Depth: Fusing Transformers and CNNs for All-day Self-supervised Monocular Depth Estimation
Zezheng Zhang, Ryan K. Y. Chan, Kenneth K. Y. Wong
In recent years, self-supervised monocular depth estimation has drawn much attention since it frees of depth annotations and achieved remarkable results on standard benchmarks. How…