In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Abstract Graph Convolutional Network (GCN) which models the potential relationship
between non-Euclidean spatial data has attracted researchers' attention in deep learning in …

The task of multi-label image recognition is to predict a set of object labels that present in an
image. As objects normally co-occur in an image, it is desirable to model the label …

We consider the problem of representation learning for graph data. Convolutional neural
networks can naturally operate on images, but have significant challenges in dealing with …

Estimating out-of-distribution (OOD) uncertainty is a major challenge for safely deploying
machine learning models in the open-world environment. Improved methods for OOD …

Recognizing multiple labels of images is a practical and challenging task, and significant
progress has been made by searching semantic-aware regions and modeling label …

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-
trained models (eg CLIP) to various downstream tasks in data-limited or label-limited …

Medical imaging is widely used in clinical practice for diagnosis and treatment. Report-
writing can be error-prone for unexperienced physicians, and time-consuming and tedious …

Extreme multi-label text classification (XMTC) refers to the problem of assigning to each
document its most relevant subset of class labels from an extremely large label collection …

Recent advances in clothes recognition have been driven by the construction of clothes
datasets. Existing datasets are limited in the amount of annotations and are difficult to cope …