9 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CV2021
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data
Jialei Xu, Yuanchao Bai, Xianming Liu +2
Depth estimation from a single image is an active research topic in computer vision. The most accurate approaches are based on fully supervised learning models, which rely on a lar…
cs.CV2021★ 9 cited
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network
Yuanzhi Wang, Tao Lu, Yanduo Zhang +4
Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e.g., facial parsing maps, faci…
cs.LG2021
Learning with Noisy Labels via Sparse Regularization
Xiong Zhou, Xianming Liu, Chenyang Wang +3
Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer fr…