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Grounding the Meaning of Words with Visual Attributes

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Visual Attributes

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

We address the problem of grounding representations of word meaning. Our approach learns higher level representations in a stacked autoencoder architecture 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. has_legs, is_yellow) from images, we train attribute classifiers by using our large-scale taxonomy of 600 visual attributes, representing more than 500 concepts and 700 K images. We extract textual attributes (e.g. bird, breed) from text with an existing distributional model. Experimental results on tasks related to word similarity show that the attribute-based vectors can be usefully integrated by our stacked autoencoder model to create bimodal 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 [89]).

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Notes

  1. 1.

    We use the term word to denote any sequence of non-delimiting symbols.

  2. 2.

    We use the term concept to denote the mental representation of objects belonging to basic-level classes (e.g. dog), and the term category to refer to superordinate-level classes (e.g. animal).

  3. 3.

    By the term attributes we refer to semantic properties or characteristics of concepts (or categories), expressed by words which people would use to describe their meaning.

  4. 4.

    Available at http://homepages.inf.ed.ac.uk/csilbere/resources.html.

  5. 5.

    In the context of semantic representations, attributes are often called features or properties in the literature. For the sake of consistency of the present work, we will adhere to the former term.

  6. 6.

    They are often termed semantic feature production norms (e.g. [66]) or property norms (e.g. [24]) in the literature.

  7. 7.

    Available at http://www.image-net.org.

  8. 8.

    Available at http://homepages.inf.ed.ac.uk/s1151656/resources.html.

  9. 9.

    The code by [28] is available at http://vision.cs.uiuc.edu/attributes/ (last accessed in May 2015).

  10. 10.

    Threshold values ranged from 0 to 0.9 with 0.1 stepsize.

  11. 11.

    For simplicity, we use the symbol w to denote both, the concept and its index. Analogously, symbol a denotes the attribute and its index.

  12. 12.

    The software is available at http://clic.cimec.unitn.it/strudel/.

  13. 13.

    In a one-hot vector (a.k.a. 1-of-N coding), exactly one element is one and the others are zero. In our case, the non-zero element corresponds to the object label.

  14. 14.

    See [89] for more experiments.

  15. 15.

    Available at http://homepages.inf.ed.ac.uk/s1151656/resources.html.

  16. 16.

    The corpus is downloadable from http://wacky.sslmit.unibo.it/doku.php?id=corpora.

  17. 17.

    We performed random search over combinations of hyper-parameter values.

  18. 18.

    Available at http://w3.usf.edu/FreeAssociation.

  19. 19.

    We thank Elia Bruni for providing us with their data.

  20. 20.

    From http://wacky.sslmit.unibo.it/doku.php?id=corpora.

  21. 21.

    The vectors are available at https://code.google.com/p/word2vec/.

  22. 22.

    Available at http://homepages.inf.ed.ac.uk/s0897549/data/.

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  1. Institute for Language, Cognitition and Computation, School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh, EH8 9AB, UK

    Carina Silberer

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    Rogerio Schmidt Feris

  2. IST Austria Computer Vision and Machine Learning, Klosterneuburg, Austria

    Christoph Lampert

  3. Virginia Tech Electrical and Computer Engineering, Blacksburg, Virginia, USA

    Devi Parikh

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Silberer, C. (2017). Grounding the Meaning of Words with Visual Attributes. In: Feris, R., Lampert, C., Parikh, D. (eds) Visual Attributes. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-50077-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-50077-5_13

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