14 citations · 19 across the 5 of their papers we have counts for
5 papers
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…
Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth
Nitin Bansal, Pan Ji, Junsong Yuan +1
Multi-task learning (MTL) paradigm focuses on jointly learning two or more tasks, aiming for significant improvement w.r.t model's generalizability, performance, and training/infer…
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…
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
Jiachen Liu, Pan Ji, Nitin Bansal +4
We present a novel framework named PlaneMVS for 3D plane reconstruction from multiple input views with known camera poses. Most previous learning-based plane reconstruction methods…
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…