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We initiate the first empirical study on the use of MLP architectures for vision-and-language (VL) fusion.
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
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Bottom-up and top-down attention for image captioning and visual question answering
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Visual entailment task for visually-grounded language learning
Ning Xie, Farley Lai, Derek Doran, and Asim Kadav · 2018
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training
Gen Li, Nan Duan, Yuejian Fang, Daxin Jiang, and Ming Zhou · 2019
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Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Vilbert: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Cycle-consistency for robust visual question answering
Meet Shah, Xinlei Chen, Marcus Rohrbach, and Devi Parikh · 2019
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A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi · 2019
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Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal · 2019
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Visual entailment: A novel task for fine-grained image understanding
Ning Xie, Farley Lai, Derek Doran, and Asim Kadav · 2019
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Unified vision-language pre-training for image captioning and vqa
Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J Corso, and Jianfeng Gao · 2019
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Language models are few-shot learners
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Behind the scene: Revealing the secrets of pre-trained vision-and-language models
Jize Cao, Zhe Gan, Yu Cheng, Licheng Yu, Yen-Chun Chen, and Jingjing Liu · 2020
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Dynabench: Rethinking benchmarking in nlp
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, et al · 2021
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Vilt: Vision-and-language transformer without convolution or region supervision
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Less is more: Clipbert for video-and-language learningvia sparse sampling
Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, and Jingjing Liu · 2021
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Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath R Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi · 2021
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Adversarial vqa: A new benchmark for evaluating the robustness of vqa models
Linjie Li, Jie Lei, Zhe Gan, and Jingjing Liu · 2021
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Uniter: Universal image-text representation learning
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An image is worth 16x16 words: Transformers for image recognition at scale
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Vqa-lol: Visual question answering under the lens of logic
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A closer look at the robustness of vision-and-language pre-trained models
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Oscar: Object-semantics aligned pre-training for vision-language tasks
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Value: A multi-task benchmark for video-and-language understanding evaluation
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Pay attention to mlps
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Are we ready for a new paradigm shift? a survey on visual deep mlp
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Do you even need attention? a stack of feed-forward layers does surprisingly well on imagenet
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Learning transferable visual models from natural language supervision
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How much can clip benefit vision-and-language tasks?
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Human-adversarial visual question answering
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Raftmlp: Do mlp-based models dream of winning over computer vision?
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Mlp-mixer: An all-mlp architecture for vision
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Probing inter-modality: Visual parsing with self-attention for vision-language pre-training
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