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 …

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 …

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 …

In computer vision, object detection is the classical and most challenging problem to get
accurate results in detecting objects. With the significant advancement of deep learning …

In the field of tunnel lining crack identification, the semantic segmentation algorithms based
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …

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 …

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

In this work, instead of directly predicting the pixel-level segmentation masks, the problem of
referring image segmentation is formulated as sequential polygon generation, and the …