2 citations · 5 across the 5 of their papers we have counts for
7 papers
Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detection
Junpu Wang, Guili Xu, Chunlei Li +3
Unsupervised anomaly detection using only normal samples is of great significance for quality inspection in industrial manufacturing. Although existing reconstruction-based methods…
YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images
Chenguang Liu, Guangshuai Gao, Ziyue Huang +3
Detecting objects from aerial images poses significant challenges due to the following factors: 1) Aerial images typically have very large sizes, generally with millions or even hu…
MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images
Ran Qin, Qingjie Liu, Guangshuai Gao +2
Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected. Many recently developed method…
Co-Saliency Detection with Co-Attention Fully Convolutional Network
Guangshuai Gao, Wenting Zhao, Qingjie Liu +1
Co-saliency detection aims to detect common salient objects from a group of relevant images. Some attempts have been made with the Fully Convolutional Network (FCN) framework and a…
CNN-based Density Estimation and Crowd Counting: A Survey
Guangshuai Gao, Junyu Gao, Qingjie Liu +2
Accurately estimating the number of objects in a single image is a challenging yet meaningful task and has been applied in many applications such as urban planning and public safet…
Counting dense objects in remote sensing images
Guangshuai Gao, Qingjie Liu, Yunhong Wang
Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve grea…