Grounding Distributional Semantics in the Visual World

By Marco Baroni. 2016

74 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning, Representation & Self-Supervised Learning

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Citations in the corpus

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, background): “Many practitioners believe that multimodality holds the key to problems as varied as natural language understanding [5, 41], computer vision evaluation [23], and embodied AI [64].”
Learning Visually Grounded Sentence Representations (2017, background): “It has been shown that grounding leads to improved performance on a variety of word-level tasks (Baroni, 2016; Kiela, 2017).”
Like a Baby: Visually Situated Neural Language Acquisition (2018, unknown): “…models integrate word co-occurrence data with perceptual data, either to achieve a better model of language as it exists in the minds of humans (Baroni, 2016; Johns and Jones, 2012; Kievit-Kylar and Jones, 2011; Lazaridou et al., 2014) or to improve performance on machine learning tasks such…”
Talk the Walk: Navigating New York City through Grounded Dialogue (2018, background): “On the one hand, work in multi-modal semantics has shown that grounding can lead to practical improvements on various natural language understanding tasks (see Baroni, 2016; Kiela, 2017, and references therein).”
Word meaning in minds and machines (2020, background): “There is a research area that combines computer vision with NLP models in ways that have greater promise for developing a more realistic model (see Baroni, 2016, for a review).”
A formal semantics for situated conversation (2018, background): “There are efforts in distributional semantics to learn how to ground referents for words in the visually salient scene (Baroni 2016), while work in robotics has yielded computational systems that can automatically find extra linguistic referents for referring descriptions (e.g., Kranstedt et al.…”