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Human cumulative culture and the exploitation of natural phenomena
In: ISSN: 1471-2970 ; Philosophical Transactions of the Royal Society B: Biological Sciences ; https://hal.archives-ouvertes.fr/hal-03509412 ; Philosophical Transactions of the Royal Society B: Biological Sciences, 2022, 377 (1843), ⟨10.1098/rstb.2020.0311⟩ (2022)
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2
DeepL et Google Translate face à l'ambiguïté phraséologique
In: https://hal.archives-ouvertes.fr/hal-03583995 ; 2022 (2022)
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3
Un dictionnaire peul encyclopédique de l’agriculture et de la nature
In: Cahiers du CEDIMES ; https://halshs.archives-ouvertes.fr/halshs-03648615 ; Cahiers du CEDIMES, 2022, 17 (2), pp.165-178 (2022)
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4
Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
Wei, Megan. - : Open Science Framework, 2022
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5
DanFEVER: claim verification dataset for Danish ...
Nørregaard, Jeppe; Derczynski, Leon. - : figshare, 2022
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6
DanFEVER: claim verification dataset for Danish ...
Nørregaard, Jeppe; Derczynski, Leon. - : figshare, 2022
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7
Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
Zhang, Yi. - : Purdue University Graduate School, 2022
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8
Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
Zhang, Yi. - : Purdue University Graduate School, 2022
Abstract: Empowering machines to understand our physical world should go beyond models with only natural language and models with only vision. Vision and language is a growing field of study that attempts to bridge the gap between natural language processing and computer vision communities by enabling models to learn visually grounded language. However, as an increasing number of pre-trained visual linguistic models focus on the alignment between visual regions and natural language, it is difficult to claim that these models capture certain properties of objects in their latent space, such as size. Inspired by recent trends in prompt learning, this study will design a prompt learning framework for two visual linguistic models, ViLBERT and ViLT, and use different manually crafted prompt templates to evaluate the consistency of performance of these models in comparing the size of objects. The results of this study showed that ViLT is more consistent in prediction accuracy for the given task with six pairs of objects ...
Keyword: 170203 Knowledge Representation and Machine Learning; 80104 Computer Vision; 80107 Natural Language Processing; FOS Computer and information sciences; FOS Psychology
URL: https://dx.doi.org/10.25394/pgs.19633317
https://hammer.purdue.edu/articles/thesis/Giant_Pigeon_and_Small_Person_Prompting_Visually_Grounded_Models_about_the_Size_of_Objects/19633317
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9
REYD demo files ...
Bleaman, Isaac. - : figshare, 2022
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10
REYD demo files ...
Bleaman, Isaac. - : figshare, 2022
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11
Google Colab notebook ...
Bleaman, Isaac. - : figshare, 2022
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12
REYD demo files ...
Bleaman, Isaac. - : figshare, 2022
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13
Google Colab notebook ...
Bleaman, Isaac. - : figshare, 2022
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14
REYD demo files ...
Bleaman, Isaac. - : figshare, 2022
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15
Measuring and Comparing Social Bias in Static and Contextual Word Embeddings
Mora, Alan Cueva. - : Technological University Dublin, 2022
In: Dissertations (2022)
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16
nkresearch ...
hyun, eileen. - : figshare, 2022
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17
nkresearch ...
hyun, eileen. - : figshare, 2022
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18
nkresearch ...
hyun, eileen. - : figshare, 2022
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19
nkresearch ...
hyun, eileen. - : figshare, 2022
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20
nkresearch ...
hyun, eileen. - : figshare, 2022
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