2 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.CV2022★ 2 cited
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation
Zhiqi Zhang, Nitin Bansal, Changjiang Cai +4
Synthetic datasets are often used to pretrain end-to-end optical flow networks, due to the lack of a large amount of labeled, real-scene data. But major drops in accuracy occur whe…
cs.CV2022★ 1 cited
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
Qingan Yan, Pan Ji, Nitin Bansal +3
In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner. A known issue of self-supervised depth estimation is that it…
cs.LG2018
Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?
Nitin Bansal, Xiaohan Chen, Zhangyang Wang
This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enf…