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Found 9 papers, 3 papers with code
Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Anomaly Detection
Unsupervised anomaly detection using only normal samples is of great significance for quality inspection in industrial manufacturing.
YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images
2) Small object size leads to insufficient information for effective detection.
MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images
Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected.
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images
Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention.
Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method
Object counting, whose aim is to estimate the number of objects from a given image, is an important and challenging computation task.
Co-Saliency Detection with Co-Attention Fully Convolutional Network
Co-saliency detection aims to detect common salient objects from a group of relevant images.
CNN-based Density Estimation and Crowd Counting: A Survey
Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
Counting dense objects in remote sensing images
Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied.
Defect detection for patterned fabric images based on GHOG and low-rank decomposition
In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper.
