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Titlebook: Visual Attributes; Rogerio Schmidt Feris,Christoph Lampert,Devi Parik Book 2017 Springer International Publishing AG 2017 Computer Vision.

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发表于 2025-3-21 18:20:50 | 显示全部楼层 |阅读模式
书目名称Visual Attributes
编辑Rogerio Schmidt Feris,Christoph Lampert,Devi Parik
视频videohttp://file.papertrans.cn/984/983677/983677.mp4
概述The first book to introduce the topic of visual attributes, and cover emerging concepts such as zero-shot learning.Covers theoretical aspects of visual attribute learning, as well as practical compute
丛书名称Advances in Computer Vision and Pattern Recognition
图书封面Titlebook: Visual Attributes;  Rogerio Schmidt Feris,Christoph Lampert,Devi Parik Book 2017 Springer International Publishing AG 2017 Computer Vision.
描述.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..
出版日期Book 2017
关键词Computer Vision; Fine-Grained Classification; Human-Machine Communication; Image Search and Retrieval; M
版次1
doihttps://doi.org/10.1007/978-3-319-50077-5
isbn_softcover978-3-319-84311-7
isbn_ebook978-3-319-50077-5Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightSpringer International Publishing AG 2017
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发表于 2025-3-21 22:04:49 | 显示全部楼层
In the Era of Deep Convolutional Features: Are Attributes Still Useful Privileged Data? computer vision field to solve the object recognition task in images. We want computers to be able to learn more efficiently at the expense of providing extra information during training time. In this chapter, we focus on semantic attributes as a source of additional information about image data. T
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Localizing and Visualizing Relative Attributes In contrast to traditional approaches that use global appearance features or rely on keypoint detectors, our goal is to automatically discover the image regions that are relevant to the attribute, even when the attribute’s appearance changes drastically across its attribute spectrum. To accomplish
发表于 2025-3-22 20:38:07 | 显示全部楼层
Deep Learning Face Attributes for Detection and Alignmentage, ethnicity, face shape, and nose size. Predicting face attributes in the wild is challenging due to complex face variations. This chapter aims to provide an in-depth presentation of recent progress and the current state-of-the-art approaches to solving some of the fundamental challenges in face
发表于 2025-3-22 21:39:45 | 显示全部楼层
Visual Attributes for Fashion Analyticsntroduce a system called . for recommending clothing for different occasions, and a system called . for hairstyle and facial makeup recommendation. For fashion retrieval, we describe a cross-domain clothing retrieval system, which receives as input a user photo of a particular clothing item taken in
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A Taxonomy of Part and Attribute Discovery Techniques. (PnA)-based representations are popular in computer vision as they allow modeling of appearance in a compositional manner, and provide a basis for communication between a human and a machine for various interactive applications. Based on two main properties of these techniques a unified taxonomy o
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