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 …

Textual language is the most natural carrier of human emotion. In natural language
processing, textual emotion recognition (TER) has become an important topic due to its …

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 …

Hierarchical text classification is an important yet challenging task due to the complex
structure of the label hierarchy. Existing methods ignore the semantic relationship between …

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to
its high impact in real-world applications from health and well-being to author profiling …

For the multi-label text classification problems with many classes, many existing multi-label
classification algorithms become infeasible or suffer an unaffordable cost. Some researches …

Real world videos contain many complex actions with inherent relationships between action
classes. In this work, we propose an attention-based architecture that model these action …

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 …

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 …

Deep extreme multi-label learning (XML) requires training deep architectures that can tag a
data point with its most relevant subset of labels from an extremely large label set. XML …