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• W2892282605 abstract "Several recent studies have shown the benefits of combining language and perception to infer word embeddings. These multimodal approaches either simply combine pre-trained textual and visual representations (e.g. features extracted from convolutional neural networks), or use the latter to bias the learning of textual word embeddings. In this work, we propose a novel probabilistic model to formalize how linguistic and perceptual inputs can work in concert to explain the observed word-context pairs in a text corpus. Our approach learns textual and visual representations jointly: latent visual factors couple together a skip-gram model for co-occurrence in linguistic data and a generative latent variable model for visual data. Extensive experimental studies validate the proposed model. Concretely, on the tasks of assessing pairwise word similarity and image/caption retrieval, our approach attains equally competitive or stronger results when compared to other state-of-the-art multimodal models." @default.
• W2892282605 created "2018-09-27" @default.
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• W2892282605 date "2018-01-01" @default.
• W2892282605 modified "2023-10-14" @default.
• W2892282605 title "A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images" @default.
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• W2892282605 doi "https://doi.org/10.18653/v1/d18-1177" @default.
• W2892282605 hasPublicationYear "2018" @default.
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