Diagram question answering (DQA) is an effective way to evaluate the reasoning ability for diagram semantic understanding, which is a very challenging task and largely understudied compared with natural images. 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. Min Joon Seo, Hannaneh Hajishirzi, Ali Farhadi, and Oren Etzioni. Check if you have access through your login credentials or your institution to get full access on this article. Specifically, the combination of large-scale diverse . It's Not About the Journey; It's About the Destination: Following Soft Paths Under Question-Guidance for Visual Reasoning. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. c"f~# voHdB:$|&WWU{Q[ T[lP|/.[` '24v/?I[W&n/\5P9?9X/u$![]Hu+6cnHx]lj)lb>v~1^31BWXCrW|syG e;_Qf nS,[? [Auto-]: Multi-task Dense Prediction, Robotics. Our work is most aligned with the image-language multi-task approaches [44,37,49,41,19,10,21,58]. 770--778. 2016. @CVzgtQ^zcs8X(14UFW|N(zQxBC@\yVtoqd10{{^s$:> Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension. Each caption describes the spatial relation of two individual objects in the image, and a vision-language model (VLM) needs to judge whether the caption is correctly describing the image (True) or not (False). 12-in-1: Multi-Task Vision and Language Representation Learning. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 8.3 and Sec. 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. Phuc H. Le-Khac, Graham Healy, and Alan F. Smeaton. In Computer Vision -- ECCV 2020, Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). 12-in-1: Multi-Task Vision and Language Representation Learning Abstract: 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. 12-in-1: Multi-Task Vision and Language Representation Learning Web Demo. Association for Computational Linguistics, Copenhagen, Denmark. VQA: Visual Question Answering - www.visualqa.org. In European Conference on Computer Vision. 1998. 2014. 2020. [n.d.]. Previous V&L datasets were infamous for variations in size, quality, interface, and difficulty. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. . The class PreTrainedTokenizer of PyTorch has common methods for loading/saving a tokenizer. A diagram is worth a dozen images. Unmasking Big Techs Hidden Agenda on AI Safety, How Palantir Turned a New Leaf to Profitability, 5 Cutting-Edge Language Models Transforming Healthcare, Why Enterprises Are Super Hungry for Sustainable Cloud Computing, Oracle Thinks its Ahead of Microsoft, SAP, and IBM in AI SCM, Why LinkedIns Feed Algorithm Needs a Revamp. This single model performs at par or even better than in- dependent task-specic state-of-the-art approaches for many tasks. from pytorch_transformers.tokenization_bert import BertTokenizer. This material is presented to ensure timely dissemination of scholarly and technical work. 12-in-1: Multi-Task Vision and Language Representation Learning. To the extent possible under law, Zhihong Chen has waived all copyright and related or neighboring rights to this work. Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Artificial Intelligence Review 8, 5 (1994), 349--369. The test images are removed from the train/validation set for all the tasks. Despite all the notable advancements, current KGQA systems only focus on answer generation techniques and not on answer verbalization. 2020. We thank the authors for their comprehensive review of existing studies. 12-in-1 is a multi-task model for discriminative vision-and-language tasks based on the ViLBERT (Vision and Language BERT) model. It performs four major vision-and-language tasks on its own visual question answering, caption-based image retrieval, grounding referring expressions and multi-modal verification. Are You Smarter Than a Sixth Grader? Every time a connection likes, comments, or shares content, it ends up on the users feed which at times is spam. YOLOv3: An Incremental Improvement. Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. Since many V&L (vision-and-language) tasks overlap in terms of images, a clean setup has been designed to avoid information leakage from annotations from other tasks. https://arxiv.org/abs/2012.03662. These datasets cover a wide range of tasks and require di- Need a comprehensive review of the past, present and future of modern AI research development? 2018. Are you sure you want to create this branch? Yuri Engelhardt. In the VE task, image is the premise, and text is the hypothesis. 2016. Conventional models used in this field employ common architectures to learn general Visio-linguistic representations and then fine-tune for specifically supported datasets. We are organizing the Universal Representations for Computer Vision Workshop at BMVC 2022. from vilbert.datasets import ConceptCapLoaderTrain, ConceptCapLoaderVal. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. We show through experiments that our method . For a question, there are several alternative answers. 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. The former one combines a dataset and a sampler and provides single or multi-process iterators over the training dataset. http://arxiv.org/abs/1907.11692, Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. CoRR abs/1607.06450 (2016). UNITER: UNiversal Image-TExt Representation Learning. 8.4 respectively. Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams. Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Heres a demonstration of the multi-task model implemented using Python 3 in Google colab. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. J. Comput. Your file of search results citations is now ready. [Multi-Task-Learning-PyTorch]: Multi-task Dense Prediction. Daesik Kim, YoungJoon Yoo, Jeesoo Kim, Sangkuk Lee, and Nojun Kwak. Springer, 235--251. Work fast with our official CLI. Visual diagrams and textual question-answers are interplayed in the multi-modal transformer, which achieves cross-modal semantic comprehension and reasoning. Are you sure you want to create this branch? 2020. Edit social preview. 2020. Yasuhiko Watanabe and Makoto Nagao. Theres been progressive improvement, but nobody really expected this level of human utility.. The GRE task is to localize an image region given a text reference. Figure 1:We introduce an approach for effective multi-task learn-ing, training a single model on 12 popular vision-and-languagedatasets. IEEE Computer Society Press. The steps to be followed for the implementation are as follows: !git clone 'https://github.com/facebookresearch/vilbert-multi-task'. Vis. See Call for Papers for more details! 2019. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The 12-in-1 model was proposed by Jiasen Lu, Vedanuj Goswami, Marcus Rohbach, Devi Parikh and Stefan Lee researchers from Facebook AI Research, Oregon State University and Georgia Institute of Technology in June 2020. A compelling reason to study language and vision jointly is the promise of language as a universal and natural interface for visual reasoning problems useful in both specifying a wide range of problems and communicating AI responses. If nothing happens, download Xcode and try again. The following contents are adapted from this survey. A Probing Perspective, Emmanuelle Salin, Badreddine Farah, Stephane Ayache, Benoit Favre. 2020. https://arxiv.org/abs/2103.14030. 12-in-1 is a multi-task model for discriminative vision-and-language tasks based on the ViLBERT (Vision and Language BERT) model. Researchers from the Facebook AI Research, Georgia Institute of Technology, and Oregon State University found that the skills required for different V&L tasks such as visual question answering and caption-based image retrieval overlap significantly, thanks mainly to the rise of V&L general architectures. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.). 2020. 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. 4) Set configuration path for the ResNet model. The LoadDatasetEval class loads the dataset for evaluating the model. Given an image and a natural-language question, the task is to select an answer from a fixed vocabulary. The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. If you are unfamiliar with the BERT and the ViLBERT model, you may refer to the following links before proceeding: Download our Mobile App BERT research paper BERT GitHub repository ViLBERT article ViLBERT research paper Attention is All you Need. It has also been found to have improved the average performance by 2.05 points. The model reduces the number of parameters from some 3 billion to 270 million while improving task performance by an average of 2.05 points. Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. You signed in with another tab or window. CoRR abs/1907.11692 (2019). Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. In recent years researchers in the busy deep learning, computer vision and natural language processing communities have all become increasingly interested in vision and language (V&L). [Resisual Adapater]: Multi-domain Classification. (ICML, 2020) [paper] [code], Learning to Branch for Multi-Task Learning (ICML, 2020) [paper], Partly Supervised Multitask Learning (ICMLA, 2020) paper, Understanding and Improving Information Transfer in Multi-Task Learning (ICLR, 2020) [paper], Measuring and Harnessing Transference in Multi-Task Learning (arXiv, 2020) [paper], Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition (arXiv, 2020) [paper], Learning Sparse Sharing Architectures for Multiple Tasks (AAAI, 2020) [paper], AdapterFusion: Non-Destructive Task Composition for Transfer Learning (arXiv, 2020) [paper], Adaptive Auxiliary Task Weighting for Reinforcement Learning (NeurIPS, 2019) [paper], Pareto Multi-Task Learning (NeurIPS, 2019) [paper] [code], Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains (NeurIPS, 2019) [paper], Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (NeurIPS, 2019) [paper] [code], [Orthogonal] Regularizing Deep Multi-Task Networks using Orthogonal Gradients (arXiv, 2019) [paper], Many Task Learning With Task Routing (ICCV, 2019) [paper] [code], Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels (ICCV, 2019) [paper], Deep Elastic Networks with Model Selection for Multi-Task Learning (ICCV, 2019) [paper] [code], Feature Partitioning for Efficient Multi-Task Architectures (arXiv, 2019) [paper] [code], Task Selection Policies for Multitask Learning (arXiv, 2019) [paper], BAM! 12-in-1, a multi-task vision and language representation learning approach discussed in this article is a single model run on 12 different datasets. VLN is a grounding language task of an agent's locomotion as it sees and explores the real-world dynamics based on linguistic instructions. RoBERTa: A Robustly Optimized BERT Pretraining Approach. In Proceedings of the 28th ACM International Conference on Multimedia. 1997. Please Rohini K Srihari. Palantir Technologies, the Silicon Valley analytics firm best known for its surveillance software is turning a new page in its journey. AI Technology & Industry Review syncedreview.com | Newsletter: http://bit.ly/2IYL6Y2 | Share My Research http://bit.ly/2TrUPMI | Twitter: @Synced_Global. Here we have used easydict Python library which allows dictionary values to be used as attributes. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Joseph Redmon and Ali Farhadi. Multi-task training is useful even in cases of single task scenarios. CoRR abs/1804.02767 (2018). Also, it supports an isolated analysis of each of the datasets involved. Journalist : Yuan Yuan | Editor : Michael Sarazen We know you don't want to miss any story. 8.2, Sec. Vision-Language Pretraining: Current Trends and the Future, A Survey of Vision-Language Pre-Trained Models, Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao, VLP: A Survey on Vision-Language Pre-training, Feilong Chen, Duzhen Zhang, Minglun Han, Xiuyi Chen, Jing Shi, Shuang Xu, Bo Xu, Vision-and-Language Pretrained Models: A Survey, Siqu Long, Feiqi Cao, Soyeon Caren Han, Haiqin Yang, Thong Nguyen, Cong-Duy Nguyen, Xiaobao Wu, Anh Tuan Luu, VisualBERT: A Simple and Performant Baseline for Vision and Language, Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang, ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks, Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee, LXMERT: Learning Cross-Modality Encoder Representations from Transformers, ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data, Di Qi, Lin Su, Jia Song, Edward Cui, Taroon Bharti, Arun Sacheti, InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining, Junyang Lin, An Yang, Yichang Zhang, Jie Liu, Jingren Zhou, Hongxia Yang, Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers, Zhicheng Huang, Zhaoyang Zeng, Bei Liu, Dongmei Fu, Jianlong Fu, Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models, Jize Cao, Zhe Gan, Yu Cheng, Licheng Yu, Yen-Chun Chen, Jingjing Liu, UNITER: UNiversal Image-TExt Representation Learning, Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu, Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline, Vishvak Murahari, Dhruv Batra, Devi Parikh, Abhishek Das, Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks, Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, Yejin Choi, Jianfeng Gao, X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers, Jaemin Cho, Jiasen Lu, Dustin Schwenk, Hannaneh Hajishirzi, Aniruddha Kembhavi, Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training, Gen Li, Nan Duan, Yuejian Fang, Ming Gong, Daxin Jiang, Ming Zhou, Unified Vision-Language Pre-Training for Image Captioning and VQA, Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J. Corso, Jianfeng Gao, ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph, Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang, VL-BERT: Pre-training of Generic Visual-Linguistic Representations, Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai, 12-in-1: Multi-Task Vision and Language Representation Learning, Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, Stefan Lee, Large-Scale Adversarial Training for Vision-and-Language Representation Learning, Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu, Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts, KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation, Yongfei Liu, Chenfei Wu, Shao-yen Tseng, Vasudev Lal, Xuming He, Nan Duan, VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts, Wenhui Wang, Hangbo Bao, Li Dong, Furu Wei, Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language Modeling, Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Faisal Ahmed, Zicheng Liu, Yumao Lu, Lijuan Wang, A Closer Look at the Robustness of Vision-and-Language Pre-trained Models, XGPT: Cross-modal Generative Pre-Training for Image Captioning, Qiaolin Xia, Haoyang Huang, Nan Duan, Dongdong Zhang, Lei Ji, Zhifang Sui, Edward Cui, Taroon Bharti, Xin Liu, Ming Zhou, ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration, Yuhao Cui, Zhou Yu, Chunqi Wang, Zhongzhou Zhao, Ji Zhang, Meng Wang, Jun Yu. Contrastive Representation Learning: A Framework and Review. [MTPSL]: Multi-task Partially-supervised Learning for Dense Prediction. Substantial works have. 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. We begin with an image-text matching task for very coarse instance-level alignment, and add a contrastive loss for global feature-level alignment. Arxiv Paper Link: https://arxiv.org/abs/1912.02315, If you have more questions about the project, then you can email us on team@cloudcv.org. Here, we have used Mask R-CNN model for object instance segmentation. 12-in-1: Multi-Task Vision and Language Representation Learning. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. 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 multi-modal verification. Int. 2019. It includes two subtasks, vision-to-text, and text-to-vision retrieval, where vision-to-text retrieval is to fetch the top-most relevant text description from a larger pool of descriptions as per the vision and vice versa. A great deal of vision-and-language research focuses on a small number of independent tasks of different types. Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models. If you are unfamiliar with the BERT and the ViLBERT model, you may refer to the following links before proceeding: The 12 datasets used by the model perform cover a variety of tasks which have been grouped into 4 categories as follows: The ViLBERT model forms the basis of the 12-in-1 multi-task model. However, previous research in visually-grounded language understanding have been mostly task-specific. As shown in the above figure, the single 12-in-1 model performs a variety of tasks caption and image retrieval, question answering, grounding phrases, guessing image regions based on a dialog, verifying facts about a pair of images, natural language inferences from an image, etc. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Larry O'Gorman. Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training. Southwest Jiaotong University, Chengdu, China, Institute of Automation, Chinese Academy of Sciences, Beijing, China. Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Association for Computational Linguistics, Florence, Italy, 3568--3584. Ottawa , VCR exists in the form of multiple-choice questions. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. Google Scholar Digital Library; Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, and Stefan Lee. Please try again. VideoBERT: A Joint Model for Video and Language Representation Learning. We propose a multi-task learning approach that enables to learn vision-language representation that is shared by many tasks from their diverse datasets. http://arxiv.org/abs/1607.06450. 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. Curran Associates, Inc., 22605--22618. We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. Jayant Krishnamurthy, Oyvind Taf jord, and Aniruddha Kembhavi. Diagram understanding using integration of layout information and textual information. If nothing happens, download GitHub Desktop and try again. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. arXiv:1804.02767 http://arxiv.org/abs/1804.02767. Use Git or checkout with SVN using the web URL. Zhaokai Wang, Renda Bao, Qi Wu, and Si Liu. Textbook Question Answering for Multimodal Machine Comprehension. This paper proposed a multi-modal transformer based hierarchical multi-task learning model for diagram question answering task. Most existing methods in vision language pre-training rely on object-centric features extracted through object detection, and make fine-grained alignments between the extracted features and. To have a detailed understanding about the 12-in-1 multitasking model, refer to the following sources: Discover special offers, top stories, upcoming events, and more. 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. 2019. Experiments on AI2D and FOODWEBS show the effectiveness of this method. Figure 1: We introduce an approach for effective multi-task learn- ing, training a single model on 12 popular vision-and-language datasets. Giving a visual input (image or video), VQA represents the task of correctly providing an answer to a question. 4167--4175. CoRR abs/2012.03662 (2020). 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. End-to-End Object Detection with Transformers. Deep Residual Learning for Image Recognition. 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. But the visually dependent language comprehension skills needed for these tasks to succeed overlap significantly. The latter class does the same for the validation set. The wide variety of independent V&L tasks motivated these researchers explore ways to consolidate some of them and the result of their efforts is an all-in-one model that learns from 12 supporting datasets of four broad categories of V&L tasks. Supplementary In this section, we st show the full details of the cleaned dataset in Sec. Fine-tuning the multi-task model for single tasks gives better results than the baseline single-task trained models. The ConceptCapLoaderTrain and ConceptCapLoaderVal classes have been defined here. Internally, ViLBERT uses two BERT-type models one working on text segments and the other on image regions. In recent years, there have been significant developments in Question Answering over Knowledge Graphs (KGQA). Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Taf jord. 8th International Conference on Learning Representations, . In the proposed paradigm of multi-task learning, the two tasks of diagram structural parsing and question answering are in the different semantic levels and equipped with different transformer blocks. 2)Import the required libraries and classes. We use cookies to ensure that we give you the best experience on our website. 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.). It is beginning to look like OpenAI believes that it owns the GPT technology, and has filed for a trademark on it. 2017. Copyright and all rights therein are retained by authors or by other copyright holders. Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, Kurt Keutzer, An Empirical Study of Training End-to-End Vision-and-Language Transformers, Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, Michael Zeng, Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment, Mingyang Zhou, Licheng Yu, Amanpreet Singh, Mengjiao Wang, Zhou Yu, Ning Zhang, Vision-Language Pre-Training with Triple Contrastive Learning, Jinyu Yang, Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liqun Chen, Belinda Zeng, Trishul Chilimbi, Junzhou Huang, Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework, Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang, VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix, Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo, Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision, Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig, FILIP: Fine-grained Interactive Language-Image Pre-Training, Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, Chunjing Xu, SLIP: Self-supervision meets Language-Image Pre-training, Norman Mu, Alexander Kirillov, David Wagner, Saining Xie, Learning Transferable Visual Models From Natural Language Supervision, Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever, Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP), Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt, Prototypical Contrastive Language Image Pretraining, Delong Chen, Zhao Wu, Fan Liu, Zaiquan Yang, Yixiang Huang, Yiping Bao, Erjin Zhou, Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text, Qing Li, Boqing Gong, Yin Cui, Dan Kondratyuk, Xianzhi Du, Ming-Hsuan Yang, Matthew Brown, UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning, Wei Li, Can Gao, Guocheng Niu, Xinyan Xiao, Hao Liu, Jiachen Liu, Hua Wu, Haifeng Wang, One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code, Yong Dai, Duyu Tang, Liangxin Liu, Minghuan Tan, Cong Zhou, Jingquan Wang, Zhangyin Feng, Fan Zhang, Xueyu Hu, Shuming Shi, data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language, Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli, UNIFIED-IO: A UNIFIED MODEL FOR VISION, LANGUAGE, AND MULTI-MODAL TASKS, Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, Aniruddha Kembhavi, Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks, Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Xiaogang Wang, Hongsheng Li, Xiaohua Wang, Jifeng Dai, FLAVA: A Foundational Language And Vision Alignment Model, Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, Douwe Kiela.
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