Abstract Domain generalization aims to learn a generalizable model from aknown'source
domain for variousunknown'target domains. It has been studied widely by domain …

Semantic segmentation is a key problem for many computer vision tasks. While approaches
based on convolutional neural networks constantly break new records on different …

This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained
on the source domain using only unlabeled online data from the target domain to alleviate …

Abstract Domain adaptation is crucial for transferring the knowledge from the source labeled
CT dataset to the target unlabeled MR dataset in abdominal multi-organ segmentation …

Automatic image caption prediction is a challenging task in natural language processing.
Most of the researchers have used the convolutional neural network as an encoder and …

The semantic segmentation of multitemporal remote sensing images to construct wetland
land surface coverage is the basis for the perception and dynamic modeling of geographic …

With the rapid development of convolutional neural networks (CNNs), significant progress
has been achieved in semantic segmentation. Despite the great success, such deep …

In this work, we propose a simple yet effective module called DynAmic MEan (DAME) which
allows a neural network to dynamically learn to aggregate feature maps at the pooling stage …

Large-scale image indexing and retrieval are pivotal in artificial intelligence, especially
within computer vision, for efficiently organizing and accessing extensive image databases …

In recent years, deep neural networks (DNNs) have brought great advances to various
computer vision tasks, such as image classification, object detection, semantic …