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MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models.

M. Monajatipoor, L. Li, M. Rouhsedaghat, L. Yang, and K. Chang. ACL (2), page 495-508. Association for Computational Linguistics, (2023)

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LLMs in Biomedicine: A study on clinical Named Entity Recognition.M. Monajatipoor, J. Yang, J. Stremmel, M. Emami, F. Mohaghegh, M. Rouhsedaghat, and K. Chang. CoRR, (2024)Low-resolution face recognition in resource-constrained environments.M. Rouhsedaghat, Y. Wang, S. Hu, S. You, and C. Kuo. Pattern Recognit. Lett., (2021)MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-robust Classifier.M. Rouhsedaghat, M. Monajatipoor, C. Kuo, and I. Masi. AAAI, page 2172-2179. AAAI Press, (2023)FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method.M. Rouhsedaghat, Y. Wang, X. Ge, S. Hu, S. You, and C. Kuo. ICPR Workshops (8), volume 12668 of Lecture Notes in Computer Science, page 169-183. Springer, (2020)BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis.M. Monajatipoor, M. Rouhsedaghat, L. Li, C. Kuo, A. Chien, and K. Chang. MICCAI (5), volume 13435 of Lecture Notes in Computer Science, page 725-734. Springer, (2022)Low-Resolution Face Recognition In Resource-Constrained Environments.M. Rouhsedaghat, Y. Wang, S. Hu, S. You, and C. Kuo. CoRR, (2020)BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis.M. Monajatipoor, M. Rouhsedaghat, L. Li, A. Chien, C. Kuo, F. Scalzo, and K. Chang. ICCVW, page 3327-3336. IEEE, (2021)MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier.M. Rouhsedaghat, M. Monajatipoor, K. Chang, C. Kuo, and I. Masi. CoRR, (2022)Successive Subspace Learning: An Overview.M. Rouhsedaghat, M. Monajatipoor, Z. Azizi, and C. Kuo. CoRR, (2021)DefakeHop: A Light-Weight High-Performance Deepfake Detector.H. Chen, M. Rouhsedaghat, H. Ghani, S. Hu, S. You, and C. Kuo. ICME, page 1-6. IEEE, (2021)

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MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models.

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MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models.