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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

, , , , , , , , , , , and . (2020)cite arxiv:2010.11929Comment: Fine-tuning code and pre-trained models are available at https://github.com/google-research/vision_transformer. ICLR camera-ready version with 2 small modifications: 1) Added a discussion of CLS vs GAP classifier in the appendix, 2) Fixed an error in exaFLOPs computation in Figure 5 and Table 6 (relative performance of models is basically not affected).

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Reading Twice for Natural Language Understanding.. CoRR, (2017)Neural Question Answering at BioASQ 5B., , and . BioNLP, page 76-79. Association for Computational Linguistics, (2017)An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale., , , , , , , , , and 2 other author(s). ICLR, OpenReview.net, (2021)Differentiable Patch Selection for Image Recognition., , , , , and . CVPR, page 2351-2360. Computer Vision Foundation / IEEE, (2021)Neural Domain Adaptation for Biomedical Question Answering., , and . CoNLL, page 281-289. Association for Computational Linguistics, (2017)A Light-weight & Robust System for Clinical Concept Disambiguation., , , , and . SMBM, volume 1650 of CEUR Workshop Proceedings, page 85-89. CEUR-WS.org, (2016)Neural Associative Memory for Dual-Sequence Modeling.. Rep4NLP@ACL, page 258-266. Association for Computational Linguistics, (2016)Scaling Autoregressive Video Models., , and . ICLR, OpenReview.net, (2020)DFKI: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns & Deep Verb Sense Disambiguation., , and . SemEval@NAACL-HLT, page 335-339. The Association for Computer Linguistics, (2015)Object-Centric Learning with Slot Attention., , , , , , , and . NeurIPS, (2020)