Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

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

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

Learning portable neural networks is very essential for computer vision for the purpose that
pre-trained heavy deep models can be well applied on edge devices such as mobile …

Explosive growth of big data demands efficient and fast algorithms for nearest neighbor
search. Deep learning-based hashing methods have proved their efficacy to learn advanced …

Supervised deep learning-based hash and vector quantization are enabling fast and large-
scale image retrieval systems. By fully exploiting label annotations, they are achieving …

Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

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 …

Recently, graph-based hashing that learns similarity-preserving binary codes via an affinity
graph has been extensively studied for large-scale image retrieval. However, most graph …

Compact hash code learning has been widely applied to fast similarity search owing to its
significantly reduced storage and highly efficient query speed. However, it is still a …