Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras
and LiDAR, have significant potential in precision agriculture, including object detection …

This paper presents a new algorithm for domain generalization (DG),\textit {test-time
template adjuster (T3A)}, aiming to robustify a model to unknown distribution shift. Unlike …

Data is the driving force of machine learning, with the amount and quality of training data
often being more important for the performance of a system than architecture and training …

We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-
quality semantically segmented images requiring minimal human effort. Current deep …

In this article, we propose an infrared and visible image fusion network based on the salient
target detection, termed STDFusionNet, which can preserve the thermal targets in infrared …

The topical domain generalization (DG) problem asks trained models to perform well on an
unseen target domain with different data statistics from the source training domains. In …

Abstract Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer
evidence that humans disagree, from objective tasks such as part-of-speech tagging to more …

Substantial efforts have been devoted more recently to presenting various methods for
object detection in optical remote sensing images. However, the current survey of datasets …