The ACM Digital Library is published by the Association for Computing Machinery. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Springer International Publishing, Cham, 104--120. We thank the authors for their comprehensive review of existing studies. We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. Springer International Publishing, Cham, 213--229. Contrastive Representation Learning: A Framework and Review. We invite submissions of regular and short papers. Novel Object Captioning at Scale (NoCaps). Multi-task training is useful even in cases of single task scenarios. Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers, Lisa Anne Hendricks, John Mellor, Rosalia Schneider, Jean-Baptiste Alayrac, Aida Nematzadeh, Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs, Emanuele Bugliarello, Ryan Cotterell, Naoaki Okazaki, Desmond Elliott, Unifying Vision-and-Language Tasks via Text Generation, Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal, ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision, Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training, Hongwei Xue, Yupan Huang, Bei Liu, Houwen Peng, Jianlong Fu, Houqiang Li, Jiebo Luo, Align before Fuse: Vision and Language Representation Learning with Momentum Distillation, Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, Steven Hoi, E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning, Haiyang Xu, Ming Yan, Chenliang Li, Bin Bi, Songfang Huang, Wenming Xiao, Fei Huang, Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning, Zhicheng Huang, Zhaoyang Zeng, Yupan Huang, Bei Liu, Dongmei Fu, Jianlong Fu, A Recurrent Vision-and-Language BERT for Navigation, Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez-Opazo, Stephen Gould, VinVL: Revisiting Visual Representations in Vision-Language Models, Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao, SimVLM: Simple Visual Language Model Pretraining with Weak Supervision, Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, Yuan Cao, mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections, Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, Hehong Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Contrastive Captioners are Image-Text Foundation Models, Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, Yonghui Wu, Flamingo: a Visual Language Model for Few-Shot Learning, Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, Karen Simonyan, BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation, Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi, Bridge-Tower: Building Bridges Between Encoders in Vision-Language Representation Learning, Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Nan Duan, VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation, Kaizhi Zheng, Xiaotong Chen, Odest Chadwicke Jenkins, Xin Eric Wang, MixGen: A New Multi-Modal Data Augmentation, Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li, Prefix Language Models are Unified Modal Learners, Shizhe Diao, Wangchunshu Zhou, Xinsong Zhang, Jiawei Wang, Language Models are General-Purpose Interface, Yaru Hao, Haoyu Song, Li Dong, Shaohan Huang, Zewen Chi, Wenhui Wang, Shuming Ma, Furu Wei, VL-BEIT: Generative Vision-Language Pretraining, Hangbo Bao, Wenhui Wang, Li Dong, Furu Wei, VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models, Wangchunshu Zhou, Yan Zeng, Shizhe Diao, Xinsong Zhang, VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations, Tiancheng Zhao, Tianqi Zhang, Mingwei Zhu, Haozhan Shen, Kyusong Lee, Xiaopeng Lu, Jianwei Yin, Are Vision-Language Transformers Learning Multimodal Representations? VL-BERT: Pre-training of Generic Visual-Linguistic Representations. Jize Cao, Zhe Gan, Yu Cheng, Licheng Yu, Yen-Chun Chen, and Jingjing Liu. But the visually dependent language comprehension skills needed for these tasks to succeed overlap significantly. Textbook Question Answering for Multimodal Machine Comprehension. 123, 1 (2017), 4--31. 4) Set configuration path for the ResNet model. from vilbert.datasets import ConceptCapLoaderTrain, ConceptCapLoaderVal. 2017. 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. Jayant Krishnamurthy, Oyvind Taf jord, and Aniruddha Kembhavi. Are you sure you want to create this branch? This single model performs at par or even better than in- dependent task-specic state-of-the-art approaches for many tasks. Check if you have access through your login credentials or your institution to get full access on this article. It has also been found to have improved the average performance by 2.05 points. Compared to independently trained single-task models, this represents a reduction from approximately 3 billion parameters to 270 million while simultaneously improving performance by 2.05 points on average across tasks. Southwest Jiaotong University, Chengdu, China, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 12-in-1: Multi-Task Vision and Language Representation Learning. 8.1. https://arxiv.org/abs/2103.14030. Yuri Engelhardt. 8.2, Sec. 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Vision-Language Pretraining: Current Trends and the Future Licenses To the extent possible under law, Zhihong Chen has waived all copyright and related or neighboring rights to this work. To address this problem, in this paper, we propose a novel structural parsing-integrated Hierarchical Multi-Task Learning (HMTL) model for diagram question answering based on a multi-modal transformer framework. 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. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task training regime. Given one or more images and a natural language statement, the task is to judge the correctness or predict their semantic relationship. Internally, ViLBERT uses two BERT-type models one working on text segments and the other on image regions. Association for Computational Linguistics, Copenhagen, Denmark. 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. Cai YuanQiang, Dawei Du, Libo Zhang, Longyin Wen, Weiqiang Wang, Yanjun Wu, and Siwei Lyu. Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Need a comprehensive review of the past, present and future of modern AI research development? BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2020. Deep Residual Learning for Image Recognition. 2018. The test images are removed from the train/validation set for all the tasks. Unified Vision-Language Pre-Training for Image Captioning and VQA. You signed in with another tab or window. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov.
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