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 …

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 …

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 …

While deep convolutional neural networks (CNNs) have shown a great success in single-
label image classification, it is important to note that most real world images contain multiple …

Recognizing multiple labels of an image is a practical yet challenging task, and remarkable
progress has been achieved by searching for semantic regions and exploiting label …

Multi-label image classification is a fundamental but challenging task in computer vision.
Great progress has been achieved by exploiting semantic relations between labels in recent …

This paper proposes a novel deep architecture to address multi-label image recognition, a
fundamental and practical task towards general visual understanding. Current solutions for …

Managing patients with complex multimorbidity has long been recognized as a difficult
problem due to complex disease and medication dependencies and the potential risk of …

Recognizing multiple labels of images is a fundamental but challenging task in computer
vision, and remarkable progress has been attained by localizing semantic-aware image …

With the recent advances in graph neural networks, there is a rising number of studies on
graph-based multi-label classification with the consideration of object dependencies within …