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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.
List of contents
Introduction to Visual Attributes,- Part I: Attribute-Based Recognition.- An Embarrassingly Simple Approach to Zero-Shot Learning.- In the Era of Deep Convolutional Features: Are Attributes still Useful Privileged Data?.- Divide, Share, and Conquer: Multi-Task Attribute Learning with Selective Sharing.- Part II: Relative Attributes and their Application to Image Search.- Attributes for Image Retrieval.- Fine-Grained Comparisons with Attributes.- Localizing and Visualizing Relative Attributes.- Part III: Describing People Based on Attributes.- Deep Learning Face Attributes for Detection and Alignment.- Visual Attributes for Fashion Analytics.- Part IV: Defining a Vocabulary of Attributes.- A Taxonomy of Part and Attribute Discovery Techniques.- The SUN Attribute Database: Organizing Scenes by Affordances, Materials, and Layout.- Part V: Attributes and Language.- Attributesas Semantic Units Between Natural Language and Visual Recognition.- Grounding the Meaning of Words with Visual Attributes.
About the author
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.
Summary
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.
