Visual Attributes
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Beschreibung
This unique text/reference provides a detailed overview of the latest advances in machine learning and computer vision related to visual attributes, highlighting how this emerging field intersects with other disciplines, such as computational linguistics and human-machine interaction.
Topics and features:
- Presents attribute-based methods for zero-shot classification, learning using privileged information, and methods for multi-task attribute learning
- Describes the concept of relative attributes, and examines the effectiveness of modeling relative attributes in image search applications
- Reviews state-of-the-art methods for estimation of human attributes, and describes their use in a range of different applications
- Discusses attempts to build a vocabulary of visual attributes
- Explores the connections between visual attributes and natural language
- Provides contributions from an international selection of world-renowned scientists, covering both theoretical aspects of visual attribute learning and practical computer vision applications
This authoritative work is a must-read for all researchers interested in recognizing visual attributes and using them in real-world applications, and is accessible to the wider research community in visual and semantic understanding.
Dr. Rogerio Schmidt Feris is a manager at IBM T.J. Watson Research Center, New York, USA, where he leads research in computer vision and machine learning. Dr. Christoph H. Lampert is a professor at the Institute of Science and Technology Austria, where he serves as the Principal Investigator of the Computer Vision and Machine Learning Group. Dr. Devi Parikh is an assistant professor in the School of Interactive Computing at Georgia Tech, USA, where she leads the Computer Vision Lab.
This unique text/reference provides a detailed overview of the latest advances in machine learning and computer vision related to visual attributes, highlighting how this emerging field intersects with other disciplines, such as computational linguistics and human-machine interaction. Topics and features: presents attribute-based methods for zero-shot classification, learning using privileged information, and methods for multi-task attribute learning; describes the concept of relative attributes, and examines the effectiveness of modeling relative attributes in image search applications; reviews state-of-the-art methods for estimation of human attributes, and describes their use in a range of different applications; discusses attempts to build a vocabulary of visual attributes; explores the connections between visual attributes and natural language; provides contributions from an international selection of world-renowned scientists, covering both theoretical aspects and practical applications.
The first book to introduce the topic of visual attributes, and cover emerging concepts such as zero-shot learning Covers theoretical aspects of visual attribute learning, as well as practical computer vision applications Includes contributions from world-renowned scientists in machine learning and computer vision, and at the intersection with computational linguistics and human-machine interaction Includes supplementary material: sn.pub/extras
Autor*in
Rogerio Schmidt Feris
Themen in »Visual Attributes«
Computer Vision Fine-Grained Classification Human-Machine Communication Image Search and Retrieval Machine Learning Sentence Generation from Images Visual Analysis Beyond Semantics Visual Attributes Zero-Shot Learning