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Found 5 papers, 5 papers with code
Label Propagation for Zero-shot Classification with Vision-Language Models
We leverage the graph structure of the unlabeled data and introduce ZLaP, a method based on label propagation (LP) that utilizes geodesic distances for classification.
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning
In this work, we present an approach that leverages three highly synergistic components, which are identified as key ingredients: joint classifier training with inliers and outliers, semi-supervised learning through pseudo-labeling, and model ensembling.
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)
