Introduction to Visual Attributes Rogerio Feris, Christoph Lampert, and Devi Parikh
Part I: Attribute-Based Recognition
An Embarrassingly Simple Approach to Zero-Shot Learning Bernardino Romera-Paredes and Philip H. S. Torr
In the Era of Deep Convolutional Features: Are Attributes still Useful Privileged Data? Viktoriia Sharmanska and Novi Quadrianto
Divide, Share, and Conquer: Multi-Task Attribute Learning with Selective Sharing Chao-Yeh Chen, Dinesh Jayaraman, Fei Sha, and Kristen Grauman
Part II: Relative Attributes and their Application to Image Search
Attributes for Image Retrieval Adriana Kovashka and Kristen Grauman
Fine-Grained Comparisons with Attributes Aron Yu and Kristen Grauman
Localizing and Visualizing Relative Attributes Fanyi Xiao and Yong Jae Lee
Part III: Describing People Based on Attributes
Deep Learning Face Attributes for Detection and Alignment Chen Change Loy, Ping Luo, and Chen Huang
Visual Attributes for Fashion Analytics Si Liu, Lisa Brown, Qiang Chen, Junshi Huang, Luoqi Liu, and Shuicheng Yan
Part IV: Defining a Vocabulary of Attributes
A Taxonomy of Part and Attribute Discovery Techniques Subhransu Maji
The SUN Attribute Database: Organizing Scenes by Affordances, Materials, and Layout Genevieve Patterson and James Hays
Part V: Attributes and Language
Attributes as Semantic Units Between Natural Language and Visual Recognition Marcus Rohrbach
Grounding the Meaning of Words with Visual Attributes Carina Silberer
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 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.
