This paper focuses on the task of cross domain few-shot named entity recognition (NER),
which aims to adapt the knowledge learned from source domain to recognize named entities …

Collecting well-matched multimedia datasets is crucial for training cross-modal retrieval
models. However in real-world scenarios massive multimodal data are harvested from the …

Multi-label learning with label noise presents significant real-world challenges due to
dependencies among labels, complicating the transition from clean to noisy labels …

In traditional machine learning, the training and testing data are assumed to come from the
same independent and identical distributions. This assumption, however, does not hold up …

Cross-modal retrieval (CMR) aims to establish interaction between different modalities,
among which supervised CMR is emerging due to its flexibility in learning semantic category …

Domain shift degrades classification models on new data distributions. Conventional
unsupervised domain adaptation (UDA) aims to learn features that bridge labeled source …