Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

In this paper, we study multimodal named entity recognition in social media posts. Existing
works mainly focus on using a cross-modal attention mechanism to combine text …

Multimodal emotion analysis has become a hot trend because of its wide applications, such
as the question-answering system. However, in a real-world scenario, people usually have …

Cross-domain sentiment classification aims to predict the sentiment tendency in unlabeled
target domain data using labeled source-domain data. The wide range of data sources has …

Unsupervised domain adaptation, which transfers knowledge from the source domain to the
target domain, has still been a challenging problem. However, previous domain adaptation …

Unsupervised domain adaptation aims to transfer the knowledge learned from the labeled
source domain to the unlabeled target domain, thereby improving the classification …

Out-of-distribution (OOD) detection aims to discern outliers from the intended data
distribution, which is crucial to maintaining high reliability and a good user experience. Most …

LiDAR point cloud panoptic segmentation, including both semantic and instance
segmentation, plays a critical role in meticulous scene understanding for autonomous …

Contrastive learning is a promising approach to unsupervised learning, as it inherits the
advantages of well-studied deep models without a dedicated and complex model design. In …