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 …

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

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 …

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 …

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 …

We present a new loss function called Distribution-Balanced Loss for the multi-label
recognition problems that exhibit long-tailed class distributions. Compared to conventional …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …