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Radford, A., Kim, J.W., Hallacy, C., et al. (2021) Learning Transferable Visual Models from Natural Language Supervision. arXiv: 2103.00020.
https://doi.org/10.48550/arXiv.2103.00020

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• TITLE: Dixit Player with Open CLIP

  AUTHORS: Ryan Wei

  KEYWORDS: Computer Vision, AI, CLIP, Dixit, Open AI, Creative Gameplay, Open CLIP, Natural Language Processing, Visual Models, Game AI, Image-Text Pairing

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.11 No.4, November 29, 2023

  ABSTRACT: A computer vision approach through Open AI’s CLIP, a model capable of predicting text-image pairs, is used to create an AI agent for Dixit, a game which requires creative linking between images and text. This paper calculates baseline accuracies for both the ability to match the correct image to a hint and the ability to match up with human preferences. A dataset created by previous work on Dixit is used for testing. CLIP is utilized through the comparison of a hint to multiple images, and previous hints, achieving a final accuracy of 0.5011 which surpasses previous results.