activity
20182022
most citedDense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

348 citations · 384 across the 15 of their papers we have counts for

collaborators

20 papers

cs.CV2021

Image Quality Assessment in the Modern Age

Kede Ma, Yuming Fang

This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA). From an actionable perspective, we will first…

cs.CV20213 cited

Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping

Chenyang Le, Jiebin Yan, Yuming Fang +1

We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized. We first decompose an HDR image into a normali…

cs.MM20212 cited

Learning from Synthetic Data for Opinion-free Blind Image Quality Assessment in the Wild

Zhihua Wang, Zhi-Ri Tang, Jianguo Zhang +1

Nowadays, most existing blind image quality assessment (BIQA) models 1) are developed for synthetically-distorted images and often generalize poorly to authentic ones; 2) heavily r…

cs.CV2021

Anomaly Detection in Video Sequences: A Benchmark and Computational Model

Boyang Wan, Wenhui Jiang, Yuming Fang +2

Anomaly detection has attracted considerable search attention. However, existing anomaly detection databases encounter two major problems. Firstly, they are limited in scale. Secon…

cs.CV20211 cited

Guidance and Teaching Network for Video Salient Object Detection

Yingxia Jiao, Xiao Wang, Yu-Cheng Chou +4

Owing to the difficulties of mining spatial-temporal cues, the existing approaches for video salient object detection (VSOD) are limited in understanding complex and noisy scenario…

cs.CV2021

Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning

Boyang Wan, Yuming Fang, Xue Xia +1

Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a…