Visual Attributes

  • Rogerio Schmidt Feris
  • Christoph Lampert
  • Devi Parikh

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

Table of contents

  1. Front Matter
    Pages i-viii
  2. Rogerio Schmidt Feris, Christoph Lampert, Devi Parikh
    Pages 1-7
  3. Attribute-Based Recognition

    1. Front Matter
      Pages 9-9
    2. Bernardino Romera-Paredes, Philip H. S. Torr
      Pages 11-30
    3. Chao-Yeh Chen, Dinesh Jayaraman, Fei Sha, Kristen Grauman
      Pages 49-85
  4. Relative Attributes and Their Application to Image Search

    1. Front Matter
      Pages 87-87
    2. Adriana Kovashka, Kristen Grauman
      Pages 89-117
    3. Aron Yu, Kristen Grauman
      Pages 119-154
    4. Fanyi Xiao, Yong Jae Lee
      Pages 155-178
  5. Describing People Based on Attributes

    1. Front Matter
      Pages 179-179
    2. Chen Change Loy, Ping Luo, Chen Huang
      Pages 181-214
    3. Si Liu, Lisa M. Brown, Qiang Chen, Junshi Huang, Luoqi Liu, Shuicheng Yan
      Pages 215-243
  6. Defining a Vocabulary of Attributes

    1. Front Matter
      Pages 245-245
  7. Attributes and Language

  8. Back Matter
    Pages 363-364

About this book


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.


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

Editors and affiliations

  • Rogerio Schmidt Feris
    • 1
  • Christoph Lampert
    • 2
  • Devi Parikh
    • 3
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA
  2. 2.IST Austria Computer Vision and Machine LearningKlosterneuburgAustria
  3. 3.Virginia Tech Electrical and Computer EngineeringBlacksburgUSA

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