• Corpus ID: 85459620

Semantic Comparison of State-of-the-Art Deep Learning Methods for Image Multi-Label Classification

@article{Kubany2019SemanticCO,
  title={Semantic Comparison of State-of-the-Art Deep Learning Methods for Image Multi-Label Classification},
  author={Adam Kubany and Shimon Ben Ishay and Ruben-sacha Ohayon and Armin Shmilovici and Lior Rokach and Tomer Doitshman},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.09190}
}
Image understanding relies heavily on accurate multi-label classification. In recent years deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects. With these set of tools, various implementations of DL algorithms have been released for the public use in the form of application programming interfaces (API). In this study, we evaluate and compare 10 of the most prominent publicly available APIs in a best-of-breed challenge. The evaluation… 
Multi-label Ranking: Mining Multi-label and Label Ranking Data
TLDR
This work survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi- label classification and label ranking, and offers a few future research directions.

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