This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

In a typical multi-label setting, a picture contains on average few positive labels, and many
negative ones. This positive-negative imbalance dominates the optimization process, and …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

This paper presents a simple and effective approach to solving the multi-label classification
problem. The proposed approach leverages Transformer decoders to query the existence of …

Many representative graph neural networks, $ eg $, GPR-GNN and ChebNet, approximate
graph convolutions with graph spectral filters. However, existing work either applies …

Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …

Multi-label image recognition is a challenging computer vision task of practical use.
Progresses in this area, however, are often characterized by complicated methods, heavy …

Solving multi-label recognition (MLR) for images in the low-label regime is a challenging
task with many real-world applications. Recent work learns an alignment between textual …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …