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Towards concept visualization through image generation
Nguyen, Tien Dat ; Pecina, Pavel (advisor) ; Žabokrtský, Zdeněk (referee)
Title: Toward concept visualization through image generation Author: Tien Dat Nguyen Department: Institute of Formal and Applied Linguistics Supervisors: Pavel Pecina (Charles University in Prague), Angeliki Lazaridou, Raffaella Bernardi, Marco Baroni (University of Trento), Abstract: Computational linguistic and computer vision have a common way to embed the semantics of linguistic/visual units through vector representation. In addition, high-quality semantic representations can be effectively constructed thanks to recent advances in neural network methods. Nevertheless, the under- standing of these representations remains limited, so they need to be assessed in an intuitive way. Cross-modal mapping is mapping between vector semantic embedding of words and the visual representations of the corresponding objects from images. Inverting image representation involves learning an image inversion of visual vectors (SIFT, HOG and CNN features) to reconstruct the original one. The goal of this project is to build a complete pipeline, in which word represen- tations are transformed into image vectors using cross modal mapping and these vectors are projected to pixel space using inversion. This suggests that there might be a groundbreaking way to inspect and evaluate the semantics encoded in word representations by...
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