activity
20202022
most citedNITES: A Non-Parametric Interpretable Texture Synthesis Method

10 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2022

LGSQE: Lightweight Generated Sample Quality Evaluatoin

Ganning Zhao, Vasileios Magoulianitis, Suya You +1

Despite prolific work on evaluating generative models, little research has been done on the quality evaluation of an individual generated sample. To address this problem, a lightwe…

cs.CV20215 cited

TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation

Xuejing Lei, Ganning Zhao, Kaitai Zhang +1

An explainable, efficient and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work. Although synthesis of v…

cs.CV2021

Evaluation of Multimodal Semantic Segmentation using RGB-D Data

Jiesi Hu, Ganning Zhao, Suya You +1

Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environ…

cs.CV2021

CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks

Ganning Zhao, Jiesi Hu, Suya You +1

Current perception systems often carry multimodal imagers and sensors such as 2D cameras and 3D LiDAR sensors. To fuse and utilize the data for downstream perception tasks, robust…

cs.CV202010 cited

NITES: A Non-Parametric Interpretable Texture Synthesis Method

Xuejing Lei, Ganning Zhao, C. -C. Jay Kuo

A non-parametric interpretable texture synthesis method, called the NITES method, is proposed in this work. Although automatic synthesis of visually pleasant texture can be achieve…