activity
20172020
most citedPixel-Adaptive Convolutional Neural Networks

20 citations · 41 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20207 cited

Improving Deep Stereo Network Generalization with Geometric Priors

Jialiang Wang, Varun Jampani, Deqing Sun +3

End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar. However, large datasets of…

cs.CV20201 cited

Learnable Cost Volume Using the Cayley Representation

Taihong Xiao, Jinwei Yuan, Deqing Sun +4

Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors. How…

cs.CV20197 cited

SENSE: a Shared Encoder Network for Scene-flow Estimation

Huaizu Jiang, Deqing Sun, Varun Jampani +3

We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, dis…

cs.CV201920 cited

Pixel-Adaptive Convolutional Neural Networks

Hang Su, Varun Jampani, Deqing Sun +3

Convolutions are the fundamental building block of CNNs. The fact that their weights are spatially shared is one of the main reasons for their widespread use, but it also is a majo…

cs.CV20176 cited

Cascaded Scene Flow Prediction using Semantic Segmentation

Zhile Ren, Deqing Sun, Jan Kautz +1

Given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene. Many existing approaches…