Zhaokai Wang, Renda Bao, Qi Wu, and Si Liu. Southwest Jiaotong University, Chengdu, China, Institute of Automation, Chinese Academy of Sciences, Beijing, China. Internally, ViLBERT uses two BERT-type models one working on text segments and the other on image regions. Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually grounded language understanding skills required for success at these tasks overlap significantly. AI Technology & Industry Review syncedreview.com | Newsletter: http://bit.ly/2IYL6Y2 | Share My Research http://bit.ly/2TrUPMI | Twitter: @Synced_Global. Further, we show that finetuning task-specific models from our single multi-task model can lead to further improvements, achieving performance at or above the state-of-the-art. ON , In 2020 IEEE/CVF Conference on . Add a It has also been found to have improved the average performance by 2.05 points. In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Our approach culminates in a single model on 12 datasets from four broad categories of task including visual question answering, caption-based image retrieval, grounding referring expressions, and multimodal verification. Yuri Engelhardt. However, it is limited to the English data, and there is still a lack of large-scale dataset for multimodal pretraining in Chinese. The task form of VD is given an image (or video), a dialogue history, and a language question, and let the model generate an answer for the question. Textbook Question Answering for Multimodal Machine Comprehension. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy. Springer, 565--580. Deep Residual Learning for Image Recognition. Compared to a set of independent state-of-the-art models each used for a specific V&L task, the improved ViLBERT model represents a reduction from 3 billion parameters to 270 million. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). from vilbert.datasets import ConceptCapLoaderTrain, ConceptCapLoaderVal. A curated list of vision-and-language pre-training (VLP). http://arxiv.org/abs/1907.11692, Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. In Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part VI (Lecture Notes in Computer Science), Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds. We show through experiments that our method . 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