Task-wise sampling convolutions for arbitrary-oriented object detection in aerial images
Arbitrary-oriented object detection (AOOD) has been widely applied to locate and classify
objects with diverse orientations in remote sensing images. However, the inconsistent …
objects with diverse orientations in remote sensing images. However, the inconsistent …
Achieving efficient feature representation for modulation signal: A cooperative contrast learning approach
Seamless Internet of Things (IoT) connections expose many vulnerabilities in wireless
networks, and IoT devices inevitably face many malicious active attacks. automatic …
networks, and IoT devices inevitably face many malicious active attacks. automatic …
Hyperspectral image classification using geometric spatial–spectral feature integration: A class incremental learning approach
Hyperspectral image classification (HSIC) has attracted widespread attention due to its
important application in environment alterations and geophysical disaster monitoring …
important application in environment alterations and geophysical disaster monitoring …
A Multiscale Discriminative Attack Method for Automatic Modulation Classification
Automatic Modulation Classification (AMC)-oriented Deep Neural Networks (ADNNs) have
received much attention in recent years for their wide range of applications. However, they …
received much attention in recent years for their wide range of applications. However, they …
R-CCF: region-aware continual contrastive fusion for weakly supervised object detection
Weakly-supervised learning has emerged as a compelling method for object detection by
reducing the fully annotated labels requirement in the training procedure. Recently, some …
reducing the fully annotated labels requirement in the training procedure. Recently, some …
Robust Instance-Based Semi-Supervised Learning Change Detection for Remote Sensing Images
Semi-supervised change detection (SSCD) has experienced rapid development, with
numerous semi-supervised methods being proposed to reduce the reliance on labeled data …
numerous semi-supervised methods being proposed to reduce the reliance on labeled data …
Cross-dataset Model Training for Hyperspectral Image Classification Using Self-supervised Learning
J Bai, Z Zhou, Z Chen, Z **ao, E Wei… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
With the development of deep learning and the increase in the amount of data, general
artificial intelligence models have become a popular research area nowadays. When facing …
artificial intelligence models have become a popular research area nowadays. When facing …