Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

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 …

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 investigate the problem of producing structured graph representations of visual scenes.
Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We …

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 …

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning
platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop …

Molecular classification has transformed the management of brain tumors by enabling more
accurate prognostication and personalized treatment. However, timely molecular diagnostic …

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 …

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 …