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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding
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Measuring Social Biases in Grounded Vision and Language Embeddings ...
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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ...
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Measuring Social Biases in Grounded Vision and Language Embeddings
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Ross, Candace; Barbu, Andrei; Katz, Boris. - : Center for Brains, Minds and Machines (CBMM), Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), 2021
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Learning a natural-language to LTL executable semantic parser for grounded robotics
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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ...
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Learning a natural-language to LTL executable semantic parser for grounded robotics ...
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Measuring Social Biases in Grounded Vision and Language Embeddings ...
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Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
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Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context ...
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Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context
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In: Other repository (2018)
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Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
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Do You See What I Mean? Visual Resolution of Linguistic Ambiguities ...
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Seeing What You’re Told: Sentence-Guided Activity Recognition In Video
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The Compositional Nature of Event Representations in the Human Brain
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Barbu, Andrei; Narayanaswamy, Siddharth; Xiong, Caiming; Corso, Jason J.; Fellbaum, Christiane D.; Hanson, Catherine; Hanson, Stephen Jose; Helie, Sebastien; Malaia, Evguenia; Pearlmutter, Barak A.; Siskind, Jeffrey Mark; Talavage, Thomas Michael; Wilbur, Ronnie B.. - : Center for Brains, Minds and Machines (CBMM), arXiv, 2014
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Abstract:
How does the human brain represent simple compositions of constituents: actors, verbs, objects, directions, and locations? Subjects viewed videos during neuroimaging (fMRI) sessions from which sentential descriptions of those videos were identified by decoding the brain representations based only on their fMRI activation patterns. Constituents (e.g., fold and shirt) were independently decoded from a single presentation. Independent constituent classification was then compared to joint classification of aggregate concepts (e.g., fold -shirt); results were similar as measured by accuracy and correlation. The brain regions used for independent constituent classification are largely disjoint and largely cover those used for joint classification. This allows recovery of sentential descriptions of stimulus videos by composing the results of the independent constituent classifiers. Furthermore, classifiers trained on the words one set of subjects think of when watching a video can recognize sentences a different subject thinks of when watching a different video. ; This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.
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Keyword:
Computer Language; Language; Linguistics; Neuroscience; Vision and Language
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URL: http://hdl.handle.net/1721.1/100175
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Saying What You're Looking For: Linguistics Meets Video Search ...
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