In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020. Given a caption and a pool of images, the task is to retrieve the target image that is best described by the caption. J. Comput. 12-in-1: Multi-Task Vision and Language Representation Learning 8. 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. 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. 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 multimodal verification. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task training regime. 12-in-1: Multi-Task Vision and Language Representation Learning. The following contents are adapted from this survey. 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. Layer Normalization. Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. UNITER: UNiversal Image-TExt Representation Learning. Figure 1: We introduce an approach for effective multi-task learn- ing, training a single model on 12 popular vision-and-language datasets. Subscribe to our popular Synced Global AI Weekly to get weekly AI updates. arXiv:1804.02767 http://arxiv.org/abs/1804.02767. 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. Research Areas Impact Notable Papers Publications Fundamental & Applied Request for Proposals Projects. Please We propose a multi-task learning approach that enables to learn vision-language representation that is shared by many tasks from their diverse datasets. Journalist : Yuan Yuan | Editor : Michael Sarazen We know you don't want to miss any story. In Proceedings of the 28th ACM International Conference on Multimedia. Figure 1:We introduce an approach for effective multi-task learn-ing, training a single model on 12 popular vision-and-languagedatasets. 2019. VLN is a grounding language task of an agent's locomotion as it sees and explores the real-world dynamics based on linguistic instructions. arXiv preprint arXiv:1803.05457 (2018). 8th International Conference on Learning Representations, . M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for 709--717. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2020. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Taf jord. 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 ural language processing and computer vision. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. ViLBERT takes as input an image I and text segment Q. But the visually dependent language comprehension skills needed for these tasks to succeed overlap significantly. [MTPSL]: Multi-task Partially-supervised Learning for Dense Prediction. Check if you have access through your login credentials or your institution to get full access on this article. PDF scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. It has also been found to have improved the average performance by 2.05 points. Need a comprehensive review of the past, present and future of modern AI research development? Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The latter class does the same for the validation set. 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,[? 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. We use cookies to ensure that we give you the best experience on our website. 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. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. 12-in-1: Multi-Task Vision and Language Representation Learning There was a problem preparing your codespace, please try again. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. 770--778. 2018. Yasuhiko Watanabe and Makoto Nagao. 2002. [n.d.]. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Joseph Redmon and Ali Farhadi. 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? These CVPR 2020 papers are the Open Access versions, provided by the. The field of vision-and-language research combines vision and language to perform specialized tasks such as caption generation, each of which is supported by a few datasets. End-to-End Object Detection with Transformers. 8.3 and Sec. 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 Computer Vision -- ECCV 2020, Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). to demonstrate the benefits of pre-training in the multi-omic integration 247 task. A diagram is worth a dozen images. 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. 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. ICLR (2021). IEEE, 10434--10443. Your file of search results citations is now ready. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Are you sure you want to create this branch? We invite submissions of regular and short papers. In recent years, there have been significant developments in Question Answering over Knowledge Graphs (KGQA). (NeurIPS, 2022) [paper], Task Discovery: Finding the Tasks that Neural Networks Generalize on (NeurIPS, 2022) [paper], [Auto-] Auto-: Disentangling Dynamic Task Relationships (TMLR, 2022) [paper] [code], [Universal Representations] Universal Representations: A Unified Look at Multiple Task and Domain Learning (arXiv, 2022) [paper] [code], MTFormer: Multi-Task Learning via Transformer and Cross-Task Reasoning (ECCV, 2022) [paper], Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space (ECCV, 2022) [paper] [code], Factorizing Knowledge in Neural Networks (ECCV, 2022) [paper] [code], [InvPT] Inverted Pyramid Multi-task Transformer for Dense Scene Understanding (ECCV, 2022) [paper] [code], [MultiMAE] MultiMAE: Multi-modal Multi-task Masked Autoencoders (ECCV, 2022) [paper] [code], A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity (ICML, 2022) [paper], Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization (ICML, 2022) [paper], Active Multi-Task Representation Learning (ICML, 2022) [paper], Generative Modeling for Multi-task Visual Learning (ICML, 2022) [paper] [code], Multi-Task Learning as a Bargaining Game (ICML, 2022) [paper] [code], Multi-Task Learning with Multi-query Transformer for Dense Prediction (arXiv, 2022) [paper], [Gato] A Generalist Agent (arXiv, 2022) [paper], [MTPSL] Learning Multiple Dense Prediction Tasks from Partially Annotated Data (CVPR, 2022) [paper] [code], [TSA] Cross-domain Few-shot Learning with Task-specific Adapters (CVPR, 2022) [paper] [code], [OMNIVORE] OMNIVORE: A Single Model for Many Visual Modalities (CVPR, 2022) [paper] [code], Task Adaptive Parameter Sharing for Multi-Task Learning (CVPR, 2022) [paper], Controllable Dynamic Multi-Task Architectures (CVPR, 2022) [paper] [code], [SHIFT] SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation (CVPR, 2022) [paper] [code], DiSparse: Disentangled Sparsification for Multitask Model Compression (CVPR, 2022) [paper] [code], [MulT] MulT: An End-to-End Multitask Learning Transformer (CVPR, 2022) [paper] [code], Sound and Visual Representation Learning with Multiple Pretraining Tasks (CVPR, 2022) [paper], Medusa: Universal Feature Learning via Attentional Multitasking (CVPR Workshop, 2022) [paper], An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems (arXiv, 2022) [paper] [code], Combining Modular Skills in Multitask Learning (arXiv, 2022) [paper], Visual Representation Learning over Latent Domains (ICLR, 2022) [paper], ADARL: What, Where, and How to Adapt in Transfer Reinforcement Learning (ICLR, 2022) [paper] [code], Towards a Unified View of Parameter-Efficient Transfer Learning (ICLR, 2022) [paper] [code], [Rotograd] Rotograd: Dynamic Gradient Homogenization for Multi-Task Learning (ICLR, 2022) [paper] [code], Relational Multi-task Learning: Modeling Relations Between Data and Tasks (ICLR, 2022) [paper], Weighted Training for Cross-task Learning (ICLR, 2022) [paper] [code], Semi-supervised Multi-task Learning for Semantics and Depth (WACV, 2022) [paper], In Defense of the Unitary Scalarization for Deep Multi-Task Learning (arXiv, 2022) [paper], Variational Multi-Task Learning with Gumbel-Softmax Priors (NeurIPS, 2021) [paper] [code], Efficiently Identifying Task Groupings for Multi-Task Learning (NeurIPS, 2021) [paper], [CAGrad] Conflict-Averse Gradient Descent for Multi-task Learning (NeurIPS, 2021) [paper] [code], A Closer Look at Loss Weighting in Multi-Task Learning (arXiv, 2021) [paper], Exploring Relational Context for Multi-Task Dense Prediction (ICCV, 2021) [paper] [code], Multi-Task Self-Training for Learning General Representations (ICCV, 2021) [paper], Task Switching Network for Multi-task Learning (ICCV, 2021) [paper] [code], Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV, 2021) [paper] [project], Robustness via Cross-Domain Ensembles (ICCV, 2021) [paper] [code], Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation (ICCV, 2021) [paper] [code], [URL] Universal Representation Learning from Multiple Domains for Few-shot Classification (ICCV, 2021) [paper] [code], [tri-M] A Multi-Mode Modulator for Multi-Domain Few-Shot Classification (ICCV, 2021) [paper] [code], MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach (ICCV Workshop, 2021) [paper], See Yourself in Others: Attending Multiple Tasks for Own Failure Detection (arXiv, 2021) [paper], A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation (CinC, 2021) [paper] [code], Multi-Task Reinforcement Learning with Context-based Representations (ICML, 2021) [paper], [FLUTE] Learning a Universal Template for Few-shot Dataset Generalization (ICML, 2021) [paper] [code], Towards a Unified View of Parameter-Efficient Transfer Learning (arXiv, 2021) [paper], UniT: Multimodal Multitask Learning with a Unified Transformer (arXiv, 2021) [paper], Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation (CVPR, 2021) [paper] [code], CompositeTasking: Understanding Images by Spatial Composition of Tasks (CVPR, 2021) [paper] [code], Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (CVPR, 2021) [paper], Taskology: Utilizing Task Relations at Scale (CVPR, 2021) [paper], Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation (CVPR, 2021) [paper] [code], Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation (arXiv, 2021) [paper] [code], Counter-Interference Adapter for Multilingual Machine Translation (Findings of EMNLP, 2021) [paper], Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data (ICLR) [paper] [code], [Gradient Vaccine] Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models (ICLR, 2021) [paper], [IMTL] Towards Impartial Multi-task Learning (ICLR, 2021) [paper], Deciphering and Optimizing Multi-Task Learning: A Random Matrix Approach (ICLR, 2021) [paper], [URT] A Universal Representation Transformer Layer for Few-Shot Image Classification (ICLR, 2021) [paper] [code], Flexible Multi-task Networks by Learning Parameter Allocation (ICLR Workshop, 2021) [paper], Multi-Loss Weighting with Coefficient of Variations (WACV, 2021) [paper] [code], Multi-Task Reinforcement Learning with Soft Modularization (NeurIPS, 2020) [paper] [code], AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS, 2020) [paper] [code], [GradDrop] Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout (NeurIPS, 2020) [paper] [code], [PCGrad] Gradient Surgery for Multi-Task Learning (NeurIPS, 2020) [paper] [tensorflow] [pytorch], On the Theory of Transfer Learning: The Importance of Task Diversity (NeurIPS, 2020) [paper], A Study of Residual Adapters for Multi-Domain Neural Machine Translation (WMT, 2020) [paper], Multi-Task Adversarial Attack (arXiv, 2020) [paper], Automated Search for Resource-Efficient Branched Multi-Task Networks (BMVC, 2020) [paper] [code], Branched Multi-Task Networks: Deciding What Layers To Share (BMVC, 2020) [paper], MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning (ECCV, 2020) [paper] [code], Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference (ECCV, 2020) [paper] [code], Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification (ECCV, 2020) [paper] [code], Multitask Learning Strengthens Adversarial Robustness (ECCV 2020) [paper] [code], Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning (ECCV, 2020) [paper] [code], [KD4MTL] Knowledge Distillation for Multi-task Learning (ECCV Workshop) [paper] [code], MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning (CVPR, 2020) [paper] [code], Robust Learning Through Cross-Task Consistency (CVPR, 2020) [paper] [code], 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], Masking as an Efficient Alternative to Finetuning for Pretrained Language Models (EMNLP, 2020) [paper] [code], Effcient Continuous Pareto Exploration in Multi-Task Learning (ICML, 2020) [paper] [code], Which Tasks Should Be Learned Together in Multi-task Learning?
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