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Guessing objects in context

Published: 24 July 2016 Publication History

Abstract

Large scale object classification has seen commendable progress owing, in large part, to recent advances in deep learning. However, generating annotated training datasets is still a significant challenge, especially when training classifiers for large number of object categories. In these situations, generating training datasets is expensive coupled with the fact that training data may not be available for all categories and situations. Such situations are generally resolved using zero-shot learning. However, training zero-shot classifiers entails serious programming effort and is not scalable to very large number of object categories. We propose a novel simple framework that can guess objects in an image. The proposed framework has the advantages of scalability and ease of use with minimal loss in accuracy. The proposed framework answers the following question: How does one guess objects in an image from very few object detections?

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References

[1]
Lin, T. Y. Et Al. 2014. Microsoft COCO: Common objects in context. In Proc. ECCV, (pp. 740--755).
[2]
Mikolov, T. Et Al. 2013. Distributed representations of words and phrases and their compositionality. In Proc. NIPS, (pp. 3111--3119).
[3]
Mikolov, T. Et Al. 2013. Efficient estimation of word representations in vector space. In Proc. ICLR.
[4]
Rabinovich, A., Et Al. 2007. Objects in context. In Proc. IEEE ICCV, (pp. 1--8).
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Socher, R., Et Al. 2013. Zero-shot learning through cross-modal transfer. In Proc. NIPS, (pp. 935--943).
[6]
van Der Maaten, L., and Hinton, G. 2008. Visualizing data using t-SNE. In JMLR, 9 (2579-2605), 85.

Cited By

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  • (2021)Exploiting Word Embeddings for Recognition of Previously Unseen ObjectsPattern Recognition. ICPR International Workshops and Challenges10.1007/978-3-030-68780-9_27(314-329)Online publication date: 25-Feb-2021

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cover image ACM Conferences
SIGGRAPH '16: ACM SIGGRAPH 2016 Posters
July 2016
170 pages
ISBN:9781450343718
DOI:10.1145/2945078
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2016

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Author Tags

  1. object recognition
  2. word2vec

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View all
  • (2021)Exploiting Word Embeddings for Recognition of Previously Unseen ObjectsPattern Recognition. ICPR International Workshops and Challenges10.1007/978-3-030-68780-9_27(314-329)Online publication date: 25-Feb-2021

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