47 papers with code • 0 benchmarks • 4 datasets
These leaderboards are used to track progress in Multi-Label Learning
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
Region learning (RL) and multi-label learning (ML) have recently attracted increasing attentions in the field of facial Action Unit (AU) detection.
In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video.
Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods.
Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds.
Existing approaches learn from multi-label data by manipulating with identical feature set, i. e. the very instance representation of each example is employed in the discrimination processes of all class labels.