Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …

In multi-label classification, the strategy of label-specific features has been shown to be
effective to learn from multi-label examples by accounting for the distinct discriminative …

In multi-label classification, the main focus has been to develop ways of learning the
underlying dependencies between labels, and to take advantage of this at classification …

Deep ConvNets have shown great performance for single-label image classification (eg
ImageNet), but it is necessary to move beyond the single-label classification task because …

Large-scale multi-label classification datasets are commonly, and perhaps inevitably,
partially annotated. That is, only a small subset of labels are annotated per sample. Different …

We propose a recurrent neural network (RNN) based model for image multi-label
classification. Our model uniquely integrates and learning of visual attention and Long Short …

In the era of deep learning, loss functions determine the range of tasks available to models
and algorithms. To support the application of deep learning in multi-label classification …

Binary Relevance is a well-known framework for multi-label classification, which considers
each class label as a binary classification problem. Many existing multi-label algorithms are …

Multi-label classification problem is a key learning task where each instance may belong to
multiple class labels simultaneously. However, there exists four main challenges:(a) …

Label-specific features serve as an effective strategy to learn from multi-label data, where a
set of features encoding specific characteristics of each label are generated to help induce …