activity
20202025
most citedNITES: A Non-Parametric Interpretable Texture Synthesis Method

10 citations · 19 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2025

Descrip3D: Enhancing Large Language Model-based 3D Scene Understanding with Object-Level Text Descriptions

Jintang Xue, Ganning Zhao, Jie-En Yao +5

Understanding 3D scenes goes beyond simply recognizing objects; it requires reasoning about the spatial and semantic relationships between them. Current 3D scene-language models of…

eess.IV2023★ 4 cited

Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction

Wenhui Cui, Haleh Akrami, Ganning Zhao +2

Despite the impressive advancements achieved using deep-learning for functional brain activity analysis, the heterogeneity of functional patterns and scarcity of imaging data still…

cs.CV2023

SemST: Semantically Consistent Multi-Scale Image Translation via Structure-Texture Alignment

Ganning Zhao, Wenhui Cui, Suya You +1

Unsupervised image-to-image (I2I) translation learns cross-domain image mapping that transfers input from the source domain to output in the target domain while preserving its sema…

cs.CV2023

A Study on Improving Realism of Synthetic Data for Machine Learning

Tingwei Shen, Ganning Zhao, Suya You

Synthetic-to-real data translation using generative adversarial learning has achieved significant success in improving synthetic data. Yet, limited studies focus on deep evaluation…

cs.CV2023

Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints

Ganning Zhao, Tingwei Shen, Suya You +1

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-wor…

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.CV2021★ 5 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.CV2020★ 10 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…