12 in 1: multi task vision and language representation learning

2002. Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types (TPAMI, 2022) [paper], Multi-Task Learning for Dense Prediction Tasks: A Survey (TPAMI, 2021) [paper] [code], A Survey on Multi-Task Learning (TKDE, 2021) [paper], Multi-Task Learning with Deep Neural Networks: A Survey (arXiv, 2020) [paper], Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018 [best paper]) [paper] [dataset], A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks (IEEE Access, 2019) [paper], An Overview of Multi-Task Learning in Deep Neural Networks (arXiv, 2017) [paper], [NYUv2] Indoor Segmentation and Support Inference from RGBD Images (ECCV, 2012) [paper] [dataset], [Cityscapes] The Cityscapes Dataset for Semantic Urban Scene Understanding (CVPR, 2016) [paper] [dataset], [PASCAL-Context] The Role of Context for Object Detection and Semantic Segmentation in the Wild (CVPR, 2014) [paper] [dataset], [Taskonomy] Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018 [best paper]) [paper] [dataset], [KITTI] Vision meets robotics: The KITTI dataset (IJRR, 2013) [paper] dataset, [SUN RGB-D] SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (CVPR 2015) [paper] [dataset], [BDD100K] BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning (CVPR, 2020) [paper] [dataset], [Omnidata] Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV, 2021) [paper] [project], [Meta-dataset] Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (ICLR, 2020) [paper] [dataset], [Visual Domain Decathlon] Learning multiple visual domains with residual adapters (NeurIPS, 2017) [paper] [dataset], [CelebA] Deep Learning Face Attributes in the Wild (ICCV, 2015) [paper] [dataset]. J. Comput. We begin with an image-text matching task for very coarse instance-level alignment, and add a contrastive loss for global feature-level alignment. 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). Gen Li, Nan Duan, Yuejian Fang, Ming Gong, and Daxin Jiang. Research. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13--19, 2020. CoRR abs/1607.06450 (2016). 12-in-1: Multi-Task Vision and Language Representation Learning 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. 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). Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. Hierarchical Multi-Task Learning for Diagram Question Answering with :-). 12-in-1: Multi-Task Vision and Language Representation Learning (CVPR, 2020) paper [ code] A Multi-task Mean Teacher for Semi-supervised Shadow Detection (CVPR, 2020) [ paper] [ code] MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer (EMNLP, 2020) [ paper] 8.1. 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,[? 2016. It's Not About the Journey; It's About the Destination: Following Soft Paths Under Question-Guidance for Visual Reasoning. Your file of search results citations is now ready. In 2020 IEEE/CVF Conference on . 12-in-1: Multi-Task Vision and Language Representation Learning ), Vol. jP_x}sqR+.f3J,VmI? Telling juxtapositions: Using repetition and alignable difference in diagram understanding. Diagram Understanding in Geometry Questions. You signed in with another tab or window. YOLOv3: An Incremental Improvement. M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for (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! ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. 12-in-1: Multi-Task Vision and Language Representation Learning. 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? Please download or close your previous search result export first before starting a new bulk export. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Impact. Eager to grasp emerging techniques to get insights from data and hence explore realistic Data Science applications as well. Conventional models used in this field employ common architectures to learn general Visio-linguistic representations and then fine-tune for specifically supported datasets. End-to-End Object Detection with Transformers. The use of chatbots in healthcare is expected to grow due to ongoing investments in artificial intelligence and the benefits they provide, It surprised us all, including the people who are working on these things (LLMs). Oracle claimed that the company started integrating AI within its SCM system before Microsoft, IBM, and SAP. An up-to-date list of works on Multi-Task Learning. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Novel Object Captioning at Scale (NoCaps). Guided Attention Network for Object Detection and Counting on Drones. Fine-tuning the multi-task model for single tasks gives better results than the baseline single-task trained models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.). We use cookies to ensure that we give you the best experience on our website. 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. It is beginning to look like OpenAI believes that it owns the GPT technology, and has filed for a trademark on it. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch-Buc, Emily B. 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. Extensive experiments on the benchmark AI2D and FOODWEBS datasets demonstrate the effectiveness of our proposed HMTL over other state-of-the-art methods. There are three labels, Entailment, Neutral, and Contradiction. Need a comprehensive review of the past, present and future of modern AI research development? The new research not only shows the possibility of using a single model to perform multiple tasks but also proves that even with the same architecture, training with multiple datasets can actually lead to improvements on task metrics compared to single-task training. 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 CoRR abs/2012.03662 (2020). Ney H., Bowden R., Weakly supervised learning with multi-stream CNN-LSTM-HMMs to discover sequential parallelism in sign . A tag already exists with the provided branch name. Visual Reasoning and Compositional Question Answering (GQA). The steps to be followed for the implementation are as follows: !git clone 'https://github.com/facebookresearch/vilbert-multi-task'. 12-in-1: Facebook AI's New Framework Tackles Multiple Vision-and PDF 12-in-1: Multi-Task Vision and Language Representation Learning Vision 12-in-1: Multi-Task Vision and Language Representation Learning Authors: Jiasen Lu Georgia Institute of Technology Vedanuj Goswami Marcus Rohrbach Facebook AI Research Devi Parikh. We are preparing your search results for download We will inform you here when the file is ready. A great deal of vision-and-language research focuses on a small number of independent tasks of different types. Given one or more images and a natural language statement, the task is to judge the correctness or predict their semantic relationship. Figure 1:We introduce an approach for effective multi-task learn-ing, training a single model on 12 popular vision-and-languagedatasets. 12-in-1 is a multi-task model for discriminative vision-and-language tasks based on the ViLBERT (Vision and Language BERT) model. Specifically, the combination of large-scale diverse . AAAI Press, 11336--11344. Journalist: Yuan Yuan | Editor: Michael Sarazen. 123, 1 (2017), 4--31. https://arxiv.org/abs/2103.14030. 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. to demonstrate the benefits of pre-training in the multi-omic integration 247 task. 2018. Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. Based on the recently proposed ViLBERT (Vision-and-Language BERT) model for learning joint representations of image content and natural language, the new model focuses on four categories visual question answering, caption-based image retrieval, grounding referring expressions, and multi-modal verification. This paper proposed a multi-modal transformer based hierarchical multi-task learning model for diagram question answering task. sign in Springer, 565--580. Springer, 235--251. VQA: Visual Question Answering - www.visualqa.org. Jayant Krishnamurthy, Oyvind Taf jord, and Aniruddha Kembhavi. M. Haurilet, A. Roitberg, and R. Stiefelhagen. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. The paper 12-in-1: Multi-Task Vision and Language Representation Learning is available on arXiv. Please feel free to send me pull requests or email (chihung.chan@outlook.com) to add links. Here we have used easydict Python library which allows dictionary values to be used as attributes. from vilbert.datasets import ConceptCapLoaderTrain, ConceptCapLoaderVal. [MTPSL]: Multi-task Partially-supervised Learning for Dense Prediction. 8th International Conference on Learning Representations, . Artificial Intelligence Review 8, 5 (1994), 349--369. 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. The class PreTrainedTokenizer of PyTorch has common methods for loading/saving a tokenizer. [UniversalRepresentations]: Multi-task Dense Prediction (including different loss weighting strategies), Multi-domain Classification, Cross-domain Few-shot Learning. We know you dont want to miss any story. 12-in-1 is a multi-task model for discriminative vision-and-language tasks based on the ViLBERT (Vision and Language BERT) model. 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.). Given a natural language expression and an image, the task is to identify the target region that is referred to by expression (can be as simple as a noun phrase or as complex as a multi-round dialog). Authors: Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, Stefan Lee Description: Much of vision-and-language research focuses on a small but divers. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 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. 2021. Given a caption and a pool of images, the task is to retrieve the target image that is best described by the caption. Existing separate two-stage methods for DQA are limited in ineffective feedback mechanisms. In European Conference on Computer Vision. Single-Stream Multi-level Alignment for Vision-Language Pretraining Our multi-task loss consists of four tasks, engineered to align vision and language representations at multiple levels. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Taf jord. WeiHongLee/Awesome-Multi-Task-Learning - Github PDF 12-in-1: Multi-Task Vision and Language Representation Learning 2020. 8)Predict the class label using the scores, 11) Perform tokenization and detokenization of the text segments. In Computer Vision -- ECCV 2020, Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). 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. However, previous research in visually-grounded language understanding have been mostly task-specific. The model can output a score for each region, and the region with the highest score is used as the prediction region. In this paper, we explore the advantages of utilizing transformer structures for addressing multi-task learning (MTL). Multimodal pretraining has demonstrated success in the downstream tasks of cross-modal representation learning. 2020. We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Daesik Kim, YoungJoon Yoo, Jeesoo Kim, Sangkuk Lee, and Nojun Kwak. Trends of AI Technology Development Report is out! In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. As shown in Figure 4, for the 10X Multiome PBMC . 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, which constituents a hierarchical architecture. 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. 12-in-1: Multi-Task Vision and Language Representation Learning VLR involves understanding both vision (image or video) and language domains with appropriate matching strategies. [Resisual Adapater]: Multi-domain Classification. The paper 12-in-1: Multi-Task Vision and Language Representation Learning is available on arXiv. 10437-10446 Abstract Learn more. The language of graphics: A framework for the analysis of syntax and meaning in maps, charts and diagrams. 1930--1939. These CVPR 2020 papers are the Open Access versions, provided by the. The ACM Digital Library is published by the Association for Computing Machinery. ON , CoRR abs/1907.11692 (2019). This single model performs at par or even better than in-dependent task-specic state-of-the-art approaches for many tasks. [Multi-Task-Learning-PyTorch]: Multi-task Dense Prediction. 7) Define the feature extraction process. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. from pytorch_transformers.tokenization_bert import BertTokenizer. [n.d.]. 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. 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. 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. Document Image Analysis: An Executive Briefing. 12-in-1: Multi-Task Vision and Language Representation Learning Web Demo 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. Yuri Engelhardt. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. Use Git or checkout with SVN using the web URL. DiMBERT: Learning Vision-Language Grounded Representations with 12-in-1: Multi-Task Vision and Language Representation Learning Research. You signed in with another tab or window. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. . Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. The ConceptCapLoaderTrain and ConceptCapLoaderVal classes have been defined here. Find the Google colab notebook of above implementation here. 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. We produce professional, authoritative, and thought-provoking content relating to artificial intelligence, machine intelligence, emerging technologies and industrial insights. 2018 Fortune Global 500 Public Company AI Adaptivity Report is out!Purchase a Kindle-formatted report on Amazon.Apply for Insight Partner Program to get a complimentary full PDF report. 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. 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. 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. try arc, the ai2 reasoning challenge. Does Vision-and-Language Pretraining Improve Lexical Grounding? Springer International Publishing, Cham, 104--120. 1998. The configuration parameters and tasks to be done by the BERT model have been defined in the following imported classes. 12-in-1: Multi-task vision and language representation learning . For a question, there are several alternative answers. Supplementary In this section, we st show the full details of the cleaned dataset in Sec. Abstract Continuous sign language recognition (cSLR) is a public significant task that transcribes a sign language video into an ordered gloss sequence. Vis. 2019. Research Areas. [OY2bNB. Deep Residual Learning for Image Recognition. 2020. 2017. You signed in with another tab or window. 12-in-1: Multi-Task Vision and Language Representation Learning Are you sure you want to create this branch? We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. The test images are removed from the train/validation set for all the tasks. Heres a demonstration of the multi-task model implemented using Python 3 in Google colab. Palantir Technologies, the Silicon Valley analytics firm best known for its surveillance software is turning a new page in its journey. Int. Feel free to contact me or contribute if you find any interesting paper is missing! Born-Again Multi-Task Networks for Natural Language Understanding (ACL, 2019) [paper] [code], OmniNet: A unified architecture for multi-modal multi-task learning (arXiv, 2019) [paper], NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction (CVPR, 2019) [paper] [code], [MTAN + DWA] End-to-End Multi-Task Learning with Attention (CVPR, 2019) [paper] [code], Attentive Single-Tasking of Multiple Tasks (CVPR, 2019) [paper] [code], Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation (CVPR, 2019) [paper], Representation Similarity Analysis for Efficient Task Taxonomy & Transfer Learning (CVPR, 2019) [paper] [code], [Geometric Loss Strategy (GLS)] MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning (CVPR Workshop, 2019) [paper], Parameter-Efficient Transfer Learning for NLP (ICML, 2019) [paper], BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (ICML, 2019) [paper] [code], Tasks Without Borders: A New Approach to Online Multi-Task Learning (ICML Workshop, 2019) [paper], AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning (NACCL, 2019) [paper] [code], Multi-Task Deep Reinforcement Learning with PopArt (AAAI, 2019) [paper], SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning (AAAI, 2019) [paper], Latent Multi-task Architecture Learning (AAAI, 2019) [paper] [[code](https://github.com/ sebastianruder/sluice-networks)], Multi-Task Deep Neural Networks for Natural Language Understanding (ACL, 2019) [paper], Learning to Multitask (NeurIPS, 2018) [paper], [MGDA] Multi-Task Learning as Multi-Objective Optimization (NeurIPS, 2018) [paper] [code], Adapting Auxiliary Losses Using Gradient Similarity (arXiv, 2018) [paper] [code], Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (ECCV, 2018) [paper] [code], Dynamic Task Prioritization for Multitask Learning (ECCV, 2018) [paper], A Modulation Module for Multi-task Learning with Applications in Image Retrieval (ECCV, 2018) [paper], Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (KDD, 2018) [paper], Unifying and Merging Well-trained Deep Neural Networks for Inference Stage (IJCAI, 2018) [paper] [code], Efficient Parametrization of Multi-domain Deep Neural Networks (CVPR, 2018) [paper] [code], PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing (CVPR, 2018) [paper], NestedNet: Learning Nested Sparse Structures in Deep Neural Networks (CVPR, 2018) [paper], PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR, 2018) [paper] [code], [Uncertainty] Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (CVPR, 2018) [paper], Deep Asymmetric Multi-task Feature Learning (ICML, 2018) [paper], [GradNorm] GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (ICML, 2018) [paper], Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back (ICML, 2018) [paper], Gradient Adversarial Training of Neural Networks (arXiv, 2018) [paper], Auxiliary Tasks in Multi-task Learning (arXiv, 2018) [paper], Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning (ICLR, 2018) [paper] [code, Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering (ICLR, 2018) [paper], Learning multiple visual domains with residual adapters (NeurIPS, 2017) [paper] [code], Learning Multiple Tasks with Multilinear Relationship Networks (NeurIPS, 2017) [paper] [code], Federated Multi-Task Learning (NeurIPS, 2017) [paper] [code], Multi-task Self-Supervised Visual Learning (ICCV, 2017) [paper], Adversarial Multi-task Learning for Text Classification (ACL, 2017) [paper], UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory (CVPR, 2017) [paper], Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification (CVPR, 2017) [paper], Modular Multitask Reinforcement Learning with Policy Sketches (ICML, 2017) [paper] [code], SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization (ICML, 2017) [paper] [code], One Model To Learn Them All (arXiv, 2017) [paper] [code], [AdaLoss] Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing (arXiv, 2017) [paper], Deep Multi-task Representation Learning: A Tensor Factorisation Approach (ICLR, 2017) [paper] [code], Trace Norm Regularised Deep Multi-Task Learning (ICLR Workshop, 2017) [paper] [code], When is multitask learning effective?

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