Search Results for author:
Found 4 papers, 3 papers with code
Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification
In this paper we deal with the problem of detecting remote sensing images coming from a different distribution compared to the training data - out of distribution images.
Do we still need ImageNet pre-training in remote sensing scene classification?
Recently, the availability of larger high resolution remote sensing (HRRS) image datasets and progress in self-supervised learning have brought up the questions of whether supervised ImageNet pre-training is still necessary for remote sensing scene classification and would supervised pre-training on HRRS image datasets or self-supervised pre-training on ImageNet achieve better results on target remote sensing scene classification tasks.
Ranked #1 on
Multi-Label Classification
on MLRSNet
Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding
We show that, for the downstream task of remote sensing image classification, using self-supervised pre-training on remote sensing images can give better results than using supervised pre-training on images of natural scenes.
A Method for Detection of Small Moving Objects in UAV Videos
To circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds.
Ranked #1 on
Small Object Detection
on Bee4Exp Honeybee Detection
(using extra training data)
