Visual Attributes

by Rogerio Schmidt Feris (Editor), Christoph Lampert (Editor), Devi Parikh (Editor)

Overview

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.

Product Details

ISBN-13:	9783319500775
Publisher:	Springer-Verlag New York, LLC
Publication date:	03/21/2017
Series:	Advances in Computer Vision and Pattern Recognition
Sold by:	Barnes & Noble
Format:	eBook
Pages:	364
File size:	11 MB
Note:	This product may take a few minutes to download.

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.

Table of Contents

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