Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. 12-in-1: Multi-Task Vision and Language Representation Learning 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. Specifically, we leverage a transformer architecture, where two modalities are fused in a. Visual Recognition and Language Understanding are two of the challenging tasks in the domain of Artificial Intelligence. To manage your alert preferences, click on the button below. 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. 2019. (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 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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? 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. 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. We propose a multi-task learning approach that enables to learn vision-language representation that is shared by many tasks from their diverse datasets. 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. Visual Reasoning and Compositional Question Answering (GQA). 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. 2019. [UniversalRepresentations]: Multi-task Dense Prediction (including different loss weighting strategies), Multi-domain Classification, Cross-domain Few-shot Learning. 5376--5384. Among the 12 datasets are three for vocab-based VQA (VQAv2, GQA, and VGQA), two for image retrieval (COCO and Flickr30K), five for referring expressions (RefCOCO, RefCOCO+, RefCOCOG, Visual7W, and GuessWhat), and two for multi-modal verification (NLVR2 and SNLI-VE). Eager to grasp emerging techniques to get insights from data and hence explore realistic Data Science applications as well. Supplementary In this section, we st show the full details of the cleaned dataset in Sec. But the visually dependent language comprehension skills needed for these tasks to succeed overlap significantly. Int. 8.1. The model then outputs embeddings for each input. 2. PDF scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Diagram Understanding in Geometry Questions. It is to predict the affective orientation of an utterance as a continuous intensity variable. Add a 12 ural language processing and computer vision. Vision-and-Language Tasks 2.1. 2016. RACE: Large-scale ReAding Comprehension Dataset From Examinations. Unified Vision-Language Pre-Training for Image Captioning and VQA. This repo started from this survey. We know you dont want to miss any story. Here, we have used Mask R-CNN model for object instance segmentation. In NeurIPS. Cai YuanQiang, Dawei Du, Libo Zhang, Longyin Wen, Weiqiang Wang, Yanjun Wu, and Siwei Lyu. 2020. If nothing happens, download GitHub Desktop and try again. 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. Despite all the notable advancements, current KGQA systems only focus on answer generation techniques and not on answer verbalization. Deep Residual Learning for Image Recognition. 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. 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. Work fast with our official CLI. Joseph Redmon and Ali Farhadi. The ConceptCapLoaderTrain and ConceptCapLoaderVal classes have been defined here. We are organizing the Universal Representations for Computer Vision Workshop at BMVC 2022. [OY2bNB. VLN is a grounding language task of an agent's locomotion as it sees and explores the real-world dynamics based on linguistic instructions. 2002. Impact. 4167--4175. 2016. Novel Object Captioning at Scale (NoCaps). In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13--19, 2020. arXiv:1804.02767 http://arxiv.org/abs/1804.02767. The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy. 13--23. 12-in-1: Multi-Task Vision and Language Representation Learning 8. Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. Presentation video for ACM MM 2021 oral paper: Hierarchical Multi-Task Learning for Diagram Question Answering with Multi-Modal Transformer. Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams. 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. Trends of AI Technology Development Report is out! Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Hierarchical Multi-Task Learning for Diagram Question Answering with 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. [n.d.]. Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J. Corso, and Jianfeng Gao. 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?. 12-in-1: Multi-Task Vision and Language Representation Learning 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. Are You Smarter Than a Sixth Grader? Computational models for integrating linguistic and visual information: A survey.