We propose a multi-task learning approach that enables to learn vision-language representation that is shared by many tasks from their diverse datasets. The test images are thus left unmodified and the size of training data gets significantly reduced. In Computer Vision -- ECCV 2020, Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). In this paper, we propose a simple one-stage multi-task framework for visual grounding tasks. Confidence-aware Non-repetitive Multimodal Transformers for TextCaps. Are you sure you want to create this branch? 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. 12-in-1: Multi-task vision and language representation learning . 12351. It has also been found to have improved the average performance by 2.05 points. Analytics India Magazine Pvt Ltd & AIM Media House LLC 2023. Canada, MM '23: The 31st ACM International Conference on Multimedia, All Holdings within the ACM Digital Library. However, it is limited to the English data, and there is still a lack of large-scale dataset for multimodal pretraining in Chinese. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. We thank the authors for their comprehensive review of existing studies. (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! J. Comput. 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. Visual Recognition and Language Understanding are two of the challenging tasks in the domain of Artificial Intelligence. 12-in-1 is a multi-task model for discriminative vision-and-language tasks based on the ViLBERT (Vision and Language BERT) model.
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12 in 1: multi task vision and language representation learning