activity
20182023
most citedPointHop: An Explainable Machine Learning Method for Point Cloud Classification

103 citations · 446 across the 30 of their papers we have counts for

collaborators
Showing 2020Show all

14 papers · 1 filter

cs.MM2020

Study On Coding Tools Beyond Av1

Xin Zhao, Liang Zhao, Madhu Krishnan +5

The Alliance for Open Media has recently initiated coding tool exploration activities towards the next-generation video coding beyond AV1. With this regard, this paper presents a p…

cs.CV2020★ 13 cited

SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

Yuanqi Chen, Ge Li, Cece Jin +2

This paper observes that there is an issue of high frequencies missing in the discriminator of standard GAN, and we reveal it stems from downsampling layers employed in the network…

eess.IV2020★ 4 cited

A software decoder implementation for H.266/VVC video coding standard

Bin Zhu, Shan Liu, Yuan Liu +8

Versatile Video Coding Standard (H.266/VVC) was completed by Joint Video Expert Team (JVET) of ITU-T and ISO/IEC, in July 2020. This new ITU recommendation/international standard i…

cs.MM2020★ 1 cited

Overview of Screen Content Coding in Recently Developed Video Coding Standards

Xiaozhong Xu, Shan Liu

In recent years, screen content (SC) video including computer generated text, graphics and animations, have drawn more attention than ever, as many related applications become very…

eess.IV2020★ 2 cited

Referenceless Rate-Distortion Modeling with Learning from Bitstream and Pixel Features

Yangfan Sun, Li Li, Zhu Li +1

Generally, adaptive bitrates for variable Internet bandwidths can be obtained through multi-pass coding. Referenceless prediction-based methods show practical benefits compared wit…

cs.CV2020

Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)

Pranav Kadam, Min Zhang, Shan Liu +1

An unsupervised point cloud registration method, called salient points analysis (SPA), is proposed in this work. The proposed SPA method can register two point clouds effectively u…

cs.CV2020★ 1 cited

Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation

Min Zhang, Pranav Kadam, Shan Liu +1

In contrast to supervised backpropagation-based feature learning in deep neural networks (DNNs), an unsupervised feedforward feature (UFF) learning scheme for joint classification…

cs.CV2020★ 77 cited

Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation

Yurui Ren, Ge Li, Shan Liu +1

Pose-guided person image generation and animation aim to transform a source person image to target poses. These tasks require spatial manipulation of source data. However, Convolut…

cs.CV2020

Toward Zero-Shot Unsupervised Image-to-Image Translation

Yuanqi Chen, Xiaoming Yu, Shan Liu +1

Recent studies have shown remarkable success in unsupervised image-to-image translation. However, if there has no access to enough images in target classes, learning a mapping from…

cs.CV2020★ 1 cited

Learning Model-Blind Temporal Denoisers without Ground Truths

Yanghao Li, Bichuan Guo, Jiangtao Wen +3

Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises, giving way to methods that can adapt to existing noise without knowing its ground tru…

cs.SD2020★ 22 cited

AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN

Zewang Zhang, Qiao Tian, Heng Lu +2

This paper investigates how to leverage a DurIAN-based average model to enable a new speaker to have both accurate pronunciation and fluent cross-lingual speaking with very limited…

cs.SD2020★ 9 cited

FeatherWave: An efficient high-fidelity neural vocoder with multi-band linear prediction

Qiao Tian, Zewang Zhang, Heng Lu +2

In this paper, we propose the FeatherWave, yet another variant of WaveRNN vocoder combining the multi-band signal processing and the linear predictive coding. The LPCNet, a recentl…

cs.CV2020★ 2 cited

PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification

Min Zhang, Yifan Wang, Pranav Kadam +2

The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while…

eess.IV2020

Linear Model based Geometry Coding for Lidar Acquired Point Clouds

Xiang Zhang, Wen Gao, Shan Liu

In this paper, we propose a new geometry coding method for point cloud compression (PCC), where the points can be fitted and represented by straight lines. The encoding of the line…