In the world, the technological and industrial revolution is accelerating by the widespread
application of new generation information and communication technologies, such as AI, IoT …

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

Abstract Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at
adapting the model trained on a labeled source-domain dataset to a target-domain dataset …

Road segmentation from remote-sensing images is a challenging task with wide ranges of
application potentials. Deep neural networks have advanced this field by leveraging the …

Few-shot class-incremental learning is to recognize the new classes given few samples and
not forget the old classes. It is a challenging task since representation optimization and …

With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices
in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy …

Unsupervised cross-modal hashing (UCMH) has become a hot topic recently. Current
UCMH focuses on exploring data similarities. However, current UCMH methods calculate …

End-to-end text spotting aims to integrate scene text detection and recognition into a unified
framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in …

Scene graph generation (SGG) aims to detect objects and predict the relationships between
each pair of objects. Existing SGG methods usually suffer from several issues, including 1) …

Although point-based networks are demonstrated to be accurate for 3D point cloud
modeling, they are still falling behind their voxel-based competitors in 3D detection. We …