Weakly supervised object localization and detection: A survey
As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for developing new …
supervised object localization and detection plays an important role for developing new …
A deep journey into super-resolution: A survey
Deep convolutional networks–based super-resolution is a fast-growing field with numerous
practical applications. In this exposition, we extensively compare more than 30 state-of-the …
practical applications. In this exposition, we extensively compare more than 30 state-of-the …
Automatic weakly supervised object detection from high spatial resolution remote sensing images via dynamic curriculum learning
In this article, we focus on tackling the problem of weakly supervised object detection from
high spatial resolution remote sensing images, which aims to learn detectors with only …
high spatial resolution remote sensing images, which aims to learn detectors with only …
Optical remote sensing image understanding with weak supervision: Concepts, methods, and perspectives
In recent years, supervised learning has been widely used in various tasks of optical remote
sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation …
sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation …
R-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos
Vehicle detection is a significant and challenging task in aerial remote sensing applications.
Most existing methods detect vehicles with regular rectangle boxes and fail to offer the …
Most existing methods detect vehicles with regular rectangle boxes and fail to offer the …
Vehicle detection of multi-source remote sensing data using active fine-tuning network
Vehicle detection in remote sensing images has attracted increasing interest in recent years.
However, its detection ability is limited due to lack of well-annotated samples, especially in …
However, its detection ability is limited due to lack of well-annotated samples, especially in …
Methods for small, weak object detection in optical high-resolution remote sensing images: A survey of advances and challenges
Object detection that focuses on locating objects of interest and categorizing them has long
played a critical role in the development of remote sensing imagery. Following significant …
played a critical role in the development of remote sensing imagery. Following significant …
Vaid: An aerial image dataset for vehicle detection and classification
HY Lin, KC Tu, CY Li - IEEE Access, 2020 - ieeexplore.ieee.org
The availability of commercial UAVs and low-cost imaging devices has made the airborne
imagery popular and widely available. The aerial images are now extensively used for many …
imagery popular and widely available. The aerial images are now extensively used for many …
Deep multi-instance learning with dynamic pooling
End-to-end optimization of multi-instance learning (MIL) using neural networks is an
important problem with many applications, in which a core issue is how to design a …
important problem with many applications, in which a core issue is how to design a …
Region-enhanced convolutional neural network for object detection in remote sensing images
The convolutional neural networks (CNNs) have recently demonstrated to be a powerful tool
for object detection. However, with the complex scenes in remote sensing images, feature …
for object detection. However, with the complex scenes in remote sensing images, feature …