| The action similarity labeling challenge. | |
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MedLine Citation:
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PMID: 22262724 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Recognizing actions in videos is rapidly becoming a topic of much research. To facilitate the development of methods for action recognition, several video collections, along with benchmark protocols, have previously been proposed. In this paper, we present a novel video database, the "Action Similarity LAbeliNg" (ASLAN) database, along with benchmark protocols. The ASLAN set includes thousands of videos collected from the web, in over 400 complex action classes. Our benchmark protocols focus on action similarity (same/not-same), rather than action classification, and testing is performed on never-before-seen actions. We propose this data set and benchmark as a means for gaining a more principled understanding of what makes actions different or similar, rather than learning the properties of particular action classes. We present baseline results on our benchmark, and compare them to human performance. To promote further study of action similarity techniques, we make the ASLAN database, benchmarks, and descriptor encodings publicly available to the research community. |
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Authors:
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Orit Kliper-Gross; Tal Hassner; Lior Wolf |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on pattern analysis and machine intelligence Volume: 34 ISSN: 1939-3539 ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Publication Date: 2012 Mar |
Date Detail:
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Created Date: 2012-01-20 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: United States |
Other Details:
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Languages: eng Pagination: 615-21 Citation Subset: IM |
Affiliation:
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The Weizmann Institute of Science, Rehovot. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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