Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/90203
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Lüddecke, Timo;Agostini, Alejandro;Fauth, Michael;Tamošiūnaitė, Minija;Wörgötter, Florentin
Title: Distributional semantics of objects in visual scenes in comparison to text
Is part of: Artificial intelligence: an international journal. Amsterdam : Elsevier B.V, 2019, vol. 274
Extent: p. 44-65
Date: 2019
Keywords: Computer Science;Object semantics;Semantics;Distributional hypothesis
Abstract: The distributional hypothesis states that the meaning of a concept is defined through the contexts it occurs in. In practice, often word co-occurrence and proximity are analyzed in text corpora for a given word to obtain a real-valued semantic word vector, which is taken to (at least partially) encode the meaning of this word. Here we transfer this idea from text to images, where pre-assigned labels of other objects or activations of convolutional neural networks serve as context. We propose a simple algorithm that extracts and processes object contexts from an image database and yields semantic vectors for objects. We show empirically that these representations exhibit on par performance with state-of-the-art distributional models over a set of conventional objects. For this we employ well-known word benchmarks in addition to a newly proposed object-centric benchmark
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/90203/2/ISSN0004-3702_2019_V_274.PG_44-65.pdf
https://hdl.handle.net/20.500.12259/90203
https://doi.org/10.1016/j.artint.2018.12.009
Affiliation(s): Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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