State-of-the-art deep neural networks require large-scale labeled training data that is often
expensive to obtain or not available for many tasks. Weak supervision in the form of domain …

Vision-language models such as CLIP learn a generic text-image embedding from large-
scale training data. A vision-language model can be adapted to a new classification task …

Despite the success of multimodal learning in cross-modal retrieval task, the remarkable
progress relies on the correct correspondence among multimedia data. However, collecting …

As the size of the datasets getting larger, accurately annotating such datasets is becoming
more impractical due to the expensiveness on both time and economy. Therefore, crowd …

The ability to train deep neural networks under label noise is appealing, as imperfectly
annotated data are relatively cheaper to obtain. State-of-the-art approaches are based on …

Annotated data is an essential ingredient in natural language processing for training and
evaluating machine learning models. It is therefore very desirable for the annotations to be …

In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an
important role in building statistically consistent classifiers. Traditionally, the transition from …

Training deep neural networks (DNN) with noisy labels is challenging since DNN can easily
memorize inaccurate labels, leading to poor generalization ability. Recently, the meta …

Robust few-shot learning (RFSL), which aims to address noisy labels in few-shot learning,
has recently gained considerable attention. Existing RFSL methods are based on the …

Noisy labels are inevitable yet problematic in machine learning society. It ruins the
generalization of a classifier by making the classifier over-fitted to noisy labels. Existing …