activity
20202022
most citedA simple, efficient and scalable contrastive masked autoencoder for learning visual representations

12 citations · 43 across the 7 of their papers we have counts for

collaborators

6 papers

cs.CV20223 cited

MaskGIT: Masked Generative Image Transformer

Huiwen Chang, Han Zhang, Lu Jiang +2

Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative tran…

cs.CV20212 cited

SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting

Varun Jampani, Huiwen Chang, Kyle Sargent +8

Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve comp…

cs.CV2021

LASR: Learning Articulated Shape Reconstruction from a Monocular Video

Gengshan Yang, Deqing Sun, Varun Jampani +6

Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structur…

cs.CV2021

AutoFlow: Learning a Better Training Set for Optical Flow

Deqing Sun, Daniel Vlasic, Charles Herrmann +6

Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the pro…

cs.MM202110 cited

DVMark: A Deep Multiscale Framework for Video Watermarking

Xiyang Luo, Yinxiao Li, Huiwen Chang +3

Video watermarking embeds a message into a cover video in an imperceptible manner, which can be retrieved even if the video undergoes certain modifications or distortions. Traditio…

cs.MM20205 cited

Distortion Agnostic Deep Watermarking

Xiyang Luo, Ruohan Zhan, Huiwen Chang +2

Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference…