Vision-and-Language Tasks 2.1. 2016. 1998. Cloud providers prioritise sustainability in data center operations, while the IT industry needs to address carbon emissions and energy consumption. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. Multi-task learning for vision and language. VideoBERT: A Joint Model for Video and Language Representation Learning. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. 2020. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. Diagram understanding using integration of layout information and textual information. Here we have used easydict Python library which allows dictionary values to be used as attributes. The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy. 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The paper further demonstrates that multi-task training can be an effective pretraining step for single-task models as it led to further gains and set a new state-of-the-art for 7 out of 12 dataset tasks. Computational models for integrating linguistic and visual information: A survey. Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J. Corso, and Jianfeng Gao. Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Min Joon Seo, Hannaneh Hajishirzi, Ali Farhadi, and Oren Etzioni. University of Electronic Science&Technology of China, China, University of Electronic Science and Technology of China, China, https://dl.acm.org/doi/10.1145/3474085.3475255. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7--12, 2020. A tag already exists with the provided branch name. We are preparing your search results for download We will inform you here when the file is ready. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Qubec City, Qubec, Canada, Carla E. Brodley and Peter Stone (Eds.).

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