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. [OY2bNB. Here, we have used Mask R-CNN model for object instance segmentation. It is beginning to look like OpenAI believes that it owns the GPT technology, and has filed for a trademark on it. 2019. Springer International Publishing, Cham, 213--229. [Resisual Adapater]: Multi-domain Classification. The following contents are adapted from this survey. 8.4 respectively. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task model. To manage your alert preferences, click on the button below. Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. 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. Diagram understanding using integration of layout information and textual information. Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J. Corso, and Jianfeng Gao. In Computer Vision -- ECCV 2020, Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Language is an interface for visual reasoning tasks. Find the Google colab notebook of above implementation here. CoRR abs/2103.14030 (2021). In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 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. MSA is aimed to detect sentiments in videos by leveraging multi-modal signals (e.g., vision, language, etc.). 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. Please feel free to send me pull requests or email ([email protected]) to add links. A tag already exists with the provided branch name. Artificial Intelligence Review 8, 5 (1994), 349--369. try arc, the ai2 reasoning challenge. VLR involves understanding both vision (image or video) and language domains with appropriate matching strategies. 12-in-1: Multi-Task Vision and Language Representation Learning 12-in-1: Multi-Task Vision and Language Representation Learning 2016. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. . Telling juxtapositions: Using repetition and alignable difference in diagram understanding. Rohini K Srihari. End-to-End Object Detection with Transformers. This single model performs at par or even better than in-dependent task-specic state-of-the-art approaches for many tasks. 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. 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). 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. 7) Define the feature extraction process. In 2020 IEEE/CVF Conference on . 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. Edit social preview. 12-in-1: Multi-Task Vision and Language Representation Learning Your search export query has expired. 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. Daesik Kim, YoungJoon Yoo, Jeesoo Kim, Sangkuk Lee, and Nojun Kwak. 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 paper 12-in-1: Multi-Task Vision and Language Representation Learning is available on arXiv. VQA: Visual Question Answering - www.visualqa.org. 1997. [Auto-]: Multi-task Dense Prediction, Robotics. Curran Associates, Inc., 22605--22618. Multimodal pretraining has demonstrated success in the downstream tasks of cross-modal representation learning. For a question, there are several alternative answers. 2020. 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. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. On average, ne-tuning from our multi-task model for single tasks resulted in an average improvement of 2.98 points over baseline single-task trained models. Please download or close your previous search result export first before starting a new bulk export. 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. [44] combine three . Are you sure you want to create this branch? 2018. The model can output a score for each region, and the region with the highest score is used as the prediction region. Document Image Analysis: An Executive Briefing. Your file of search results citations is now ready. The former one combines a dataset and a sampler and provides single or multi-process iterators over the training dataset. Visual Reasoning and Compositional Question Answering (GQA). Phuc H. Le-Khac, Graham Healy, and Alan F. Smeaton. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. 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. 2016. UNITER: UNiversal Image-TExt Representation Learning. Impact. Such models are task-specific. (weblink). IEEE, 7463--7472. [UniversalRepresentations]: Multi-task Dense Prediction (including different loss weighting strategies), Multi-domain Classification, Cross-domain Few-shot Learning. There are three labels, Entailment, Neutral, and Contradiction. ON , 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. Min Joon Seo, Hannaneh Hajishirzi, Ali Farhadi, and Oren Etzioni. 2020. 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. Cai YuanQiang, Dawei Du, Libo Zhang, Longyin Wen, Weiqiang Wang, Yanjun Wu, and Siwei Lyu. The structural parsing module encodes the information of constituents and their relationships in diagrams, while the diagram question answering module decodes the structural signals and combines question-answers to infer correct answers. It enables the exchange of information between images and text segments. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Association for Computational Linguistics, Copenhagen, Denmark. Task-Groups and Datasets We consider 12 popular vision and language datasets. 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. Stay Connected with a larger ecosystem of data science and ML Professionals, Ethics is a human-generated thing; it gets complicated and it cannot be automated, says Wolfram Research chief Stephen Wolfram, in an exclusive and upcoming interview with AIM. In COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics. Springer International Publishing, Cham, 104--120. We propose a multi-task learning approach that enables to learn vision-language representation that is shared by many tasks from their diverse datasets. 2020. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams. to use Codespaces. Southwest Jiaotong University, Chengdu, China, Institute of Automation, Chinese Academy of Sciences, Beijing, China. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Are you sure you want to create this branch? Yuri Engelhardt. A tag already exists with the provided branch name. 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,[? We propose a multi-task learning approach that enables to learn vision-language representation that is shared by many tasks from their diverse datasets. In early work, Nguyen et al. 1998. 12-in-1: Facebook AI's New Framework Tackles Multiple Vision-and 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. For instance, the task of learning to ground the expression a yellow ball requires the same concepts as answering the question What colour is the ball?. Gen Li, Nan Duan, Yuejian Fang, Ming Gong, and Daxin Jiang. 2020. AAAI Press, 11336--11344. Multi-Grained Vision Language Pre-Training: Aligning - ResearchGate Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. In recent years, there have been significant developments in Question Answering over Knowledge Graphs (KGQA). Daesik Kim, Seonhoon Kim, and Nojun Kwak. ICLR (2021). 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. https://arxiv.org/abs/2012.03662. We produce professional, authoritative, and thought-provoking content relating to artificial intelligence, machine intelligence, emerging technologies and industrial insights. Visual Recognition and Language Understanding are two of the challenging tasks in the domain of Artificial Intelligence. 2020. (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: Multi-Task Vision and Language Representation Learning The paper 12-in-1: Multi-Task Vision and Language Representation Learning is available on arXiv. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Yasuhiko Watanabe and Makoto Nagao. Fine-tuning the multi-task model for single tasks gives better results than the baseline single-task trained models. MMT is a two-fold task of translation and text generation, translating text from one language to another with additional information from other modalities, i.e., image. Referring Transformer: A One-step Approach to Multi-task - ResearchGate Hierarchical Multi-Task Learning for Diagram Question Answering with AAAI Press, 13041--13049. 2020. Unified Vision-Language Pre-Training for Image Captioning and VQA. Are You Smarter Than a Sixth Grader? 12-in-1: Multi-Task Vision and Language Representation Learning. Substantial works have. 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. Oracle claimed that the company started integrating AI within its SCM system before Microsoft, IBM, and SAP. 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. Textbook Question Answering for Multimodal Machine Comprehension. 12-in-1: Multi-Task Vision and Language Representation Learning Web Demo 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. VLP: A Survey on Vision-Language Pre-training - ResearchGate 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. As shown in Figure 4, for the 10X Multiome PBMC . 2. Computational models for integrating linguistic and visual information: A survey. Figure 1:We introduce an approach for effective multi-task learn-ing, training a single model on 12 popular vision-and-languagedatasets. Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. Arxiv Paper Link: https://arxiv.org/abs/1912.02315, If you have more questions about the project, then you can email us on [email protected]. 2017. 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. 2014. 2019. 2019. http://arxiv.org/abs/1412.3555. Research. Jayant Krishnamurthy, Oyvind Taf jord, and Aniruddha Kembhavi. Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. 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. Visual diagrams and textual question-answers are interplayed in the multi-modal transformer, which achieves cross-modal semantic comprehension and reasoning. Canada, MM '23: The 31st ACM International Conference on Multimedia, All Holdings within the ACM Digital Library. A zealous learner aspiring to advance in the domain of AI/ML. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). It is to predict the affective orientation of an utterance as a continuous intensity variable. The configuration parameters and tasks to be done by the BERT model have been defined in the following imported classes. 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). We use cookies to ensure that we give you the best experience on our website. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch? In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task training regime. 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. 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). The model then outputs embeddings for each input. Association for Computational Linguistics, Florence, Italy, 3568--3584. 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. Larry O'Gorman. 1998. Eager to grasp emerging techniques to get insights from data and hence explore realistic Data Science applications as well. 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. 8.3 and Sec. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Joseph Redmon and Ali Farhadi. 4) Set configuration path for the ResNet model. 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. 215 cell representation learning and multiomic batch integration tasks compared to existing state-of- . Journalist : Yuan Yuan | Editor : Michael Sarazen We know you don't want to miss any story. Think you have solved question answering? Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. The input of the NLVR task is two images and a text description, and the output is whether the corresponding relationship between the images and the text description is consistent (two labels: true or false). 2017. 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. 4167--4175. Copyright and all rights therein are retained by authors or by other copyright holders. 12351. 8.2, Sec. M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for The steps to be followed for the implementation are as follows: !git clone 'https://github.com/facebookresearch/vilbert-multi-task'. 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?
