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SQuINTing at VQA Models: Introspecting VQA Models With Sub-Questions., , , , , , and . CVPR, page 10000-10008. Computer Vision Foundation / IEEE, (2020)Can domain adaptation make object recognition work for everyone?, , , and . CVPR Workshops, page 3980-3987. IEEE, (2022)SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency., , , , and . NAACL-HLT, page 3103-3111. Association for Computational Linguistics, (2021)CASTing Your Model: Learning to Localize Improves Self-Supervised Representations., , , and . CoRR, (2020)Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded., , , , , and . CoRR, (2019)CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision., , , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 8433-8447. PMLR, (2023)Align before Fuse: Vision and Language Representation Learning with Momentum Distillation., , , , , and . NeurIPS, page 9694-9705. (2021)Diverse Beam Search for Improved Description of Complex Scenes., , , , , , and . AAAI, page 7371-7379. AAAI Press, (2018)Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models., , , , , , and . CoRR, (2016)SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency., , , , and . CoRR, (2020)