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Where Next in Object Recognition and how much Supervision Do We Need?

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Advanced Topics in Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Object class recognition is an active topic in computer vision still presenting many challenges. In most approaches, this task is addressed by supervised learning algorithms that need a large quantity of labels to perform well. This leads either to small datasets (<10,000 images) that capture only a subset of the real-world class distribution (but with a controlled and verified labeling procedure), or to large datasets that are more representative but also add more label noise. Therefore, semi-supervised learning has been established as a promising direction to address object recognition. It requires only few labels while simultaneously making use of the vast amount of images available today. In this chapter, we outline the main challenges of semi-supervised object recognition, we review existing approaches, and we emphasize open issues that should be addressed next to advance this research topic.

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  1. Max Planck Institute for Informatics, Saarbrücken, Germany

    Sandra Ebert & Bernt Schiele

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  1. Sandra Ebert
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  1. Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy

    Giovanni Maria Farinella

  2. Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy

    Sebastiano Battiato

  3. Department of Engineering, University of Cambridge, Cambridge, United Kingdom

    Roberto Cipolla

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Ebert, S., Schiele, B. (2013). Where Next in Object Recognition and how much Supervision Do We Need?. In: Farinella, G., Battiato, S., Cipolla, R. (eds) Advanced Topics in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5520-1_2

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