Reinforcement learning (RL) has shown significant potential for dealing with complex
decision-making problems. However, its performance relies heavily on the availability of a …

Unsupervised domain adaptation (UDA) aims to transfer knowledge in previous and related
labeled datasets (sources) to a new unlabeled dataset (target). Despite the impressive …

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to
unlabeled target domain. Traditional methods usually achieve domain adaptation by …

Surface defect detection is very important to ensure the quality of industrial products.
Traditional machine learning cannot be well extended to a non-identically distributed …

Accurate tool wear state prediction during machining is essential for lowering production
costs and ensuring quality. Conventional deep learning-based methods perform excellently …

Abstract Domain adaptation is an important topic due to its capability in transferring
knowledge from source domain to target domain. However, many existing domain …

In recent years, the accurate recognition of traffic scenes has played a key role in
autonomous vehicle operations. However, most works in this area do not address the …

Automatic image-based weather recognition has a great significance in the practical use of
computer visual applications. The weather recognition approaches via multi-task learning …

Partial domain adaptation, which aims to transfer knowledge from a source domain with rich
labels to a unlabeled target domain where target class space is a subspace of source class …

The prohibitive cost of annotations for fully supervised 3D indoor object detection limits its
practicality. In this work we propose Random Prompt Assisted Weakly-supervised 3D Object …