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

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Grounding the Meaning of Words with Visual Attributescture from visual and textual input. The two input modalities are encoded as vectors of attributes and are obtained automatically from images and text. To obtain visual attributes (e.g. .) from images, we train attribute classifiers by using our large-scale taxonomy of 600 visual attributes, represe

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DEI

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Deep Learning Face Attributes for Detection and Alignmentributes as rich contexts to facilitate accurate face detection and face alignment in return. The chapter ends by posing an open question for the face attribute recognition challenge arising from emerging and future applications.

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The SUN Attribute Database: Organizing Scenes by Affordances, Materials, and Layoutabase and this lets us study the interplay between scene attributes and scene categories. We evaluate attribute recognition with several existing scene descriptors. Our experiments suggest that scene attributes are an efficient feature for capturing high-level semantics in scenes.

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Grounding the Meaning of Words with Visual Attributesbimodal representations which are overall more accurate than representations based on the individual modalities or different integration mechanisms (The work presented in this chapter is based on [.]).

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