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MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting.

, , , , , and . EACL, page 2515-2540. Association for Computational Linguistics, (2023)

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Computational Modeling of Word Learning: The Role of Cognitive Processes.. University of Toronto, Canada, (2016)Vision-Language Pretraining: Current Trends and the Future., , and . ACL (tutorial), page 38-43. Association for Computational Linguistics, (2022)Simple Search Algorithms on Semantic Networks Learned from Language Use., , and . CogSci, cognitivesciencesociety.org, (2016)Calculating Probabilities Simplifies Word Learning., , , and . CogSci, cognitivesciencesociety.org, (2017)Tracing the Emergence of Gendered Language in Childhood., , , , , and . CogSci, cognitivesciencesociety.org, (2020)Mutual Exclusivity as Competition in Cross-situational Word Learning., , , and . CogSci, cognitivesciencesociety.org, (2021)Desirable Difficulty in Learning: A Computational Investigation., , and . CogSci, cognitivesciencesociety.org, (2013)A Simple Recipe for Contrastively Pre-Training Video-First Encoders Beyond 16 Frames., , , , , , , , , and . CVPR, page 14386-14397. IEEE, (2024)Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization., , , , , , and . EACL (Findings), page 1171-1196. Association for Computational Linguistics, (2023)Evaluating Theory of Mind in Question Answering., , , , and . EMNLP, page 2392-2400. Association for Computational Linguistics, (2018)