activity
20182020
most citedFaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

12 citations · 31 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CV2020

Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image

Yuxing Huang, Shaodi You, Ying Fu +1

High-resolution hyperspectral images (HSIs) contain the response of each pixel in different spectral bands, which can be used to effectively distinguish various objects in complex…

cs.CV202012 cited

FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

Haotian Yang, Hao Zhu, Yanru Wang +4

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single i…

eess.IV20191 cited

Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications

Ming Lu, Ming Cheng, Yiling Xu +3

Networked video applications, e.g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth. In this paper, we have de…

eess.IV2019

Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization

Chao Huang, Haojie Liu, Tong Chen +2

We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the "prior…

eess.IV20198 cited

Gated Context Model with Embedded Priors for Deep Image Compression

Haojie Liu, Tong Chen, Peiyao Guo +2

A deep image compression scheme is proposed in this paper, offering the state-of-the-art compression efficiency, against the traditional JPEG, JPEG2000, BPG and those popular learn…

eess.IV201910 cited

Neural Video Compression using Spatio-Temporal Priors

Haojie Liu, Tong Chen, Ming Lu +2

The pursuit of higher compression efficiency continuously drives the advances of video coding technologies. Fundamentally, we wish to find better "predictions" or "priors" that are…