國立高雄大學圖資館 |

Language: English

Back

Visual attributes

Feris, Rogerio Schmidt.

Visual attributes

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visual attributesedited by Rogerio Schmidt Feris, Christoph Lampert, Devi Parikh.
other author:	Feris, Rogerio Schmidt.
Published:	Cham :Springer International Publishing :2017.
Description:	viii, 364 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computer vision.
Online resource:	http://dx.doi.org/10.1007/978-3-319-50077-5
ISBN:	9783319500775$q(electronic bk.)

Visual attributes
Visual attributes[electronic resource] /edited by Rogerio Schmidt Feris, Christoph Lampert, Devi Parikh. - Cham :Springer International Publishing :2017. - viii, 364 p. :ill., digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

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 -- Attributes as Semantic Units Between Natural Language and Visual Recognition -- Grounding the Meaning of Words with Visual Attributes.

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.

ISBN: 9783319500775$q(electronic bk.)

Standard No.: 10.1007/978-3-319-50077-5doiSubjects--Topical Terms:

200113
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Visual attributes
LDR:03869nmm a2200349 a 4500 001 508874
003 DE-He213
005 20170321164207.0
006 m d
007 cr nn 008maaau
008 171121s2017 gw s 0 eng d
020 $a 9783319500775$q(electronic bk.)
020 $a 9783319500751$q(paper)
024 7 $a 10.1007/978-3-319-50077-5 $2 doi
035 $a 978-3-319-50077-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1634
072 7 $a UYT $2 bicssc
072 7 $a UYQV $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a COM016000 $2 bisacsh
082 0 4 $a 006.37 $2 23
090 $a TA1634 $b .V834 2017
245 0 0 $a Visual attributes $h [electronic resource] / $c edited by Rogerio Schmidt Feris, Christoph Lampert, Devi Parikh.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a viii, 364 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6586
505 0 $a 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 -- Attributes as Semantic Units Between Natural Language and Visual Recognition -- Grounding the Meaning of Words with Visual Attributes.
520 $a 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.
650 0 $a Computer vision. $3 200113
650 0 $a Machine learning. $3 188639
650 0 $a Attribute (Philosophy) $3 775306
650 0 $a Categories (Philosophy) $3 269219
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a User Interfaces and Human Computer Interaction. $3 274517
700 1 $a Feris, Rogerio Schmidt. $3 775303
700 1 $a Lampert, Christoph. $3 775304
700 1 $a Parikh, Devi. $3 775305
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Advances in computer vision and pattern recognition. $3 559645
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-50077-5
950 $a Computer Science (Springer-11645)