12 in 1: multi task vision and language representation learning

This repo started from this survey. 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. (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! The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy. In Proceedings of the 28th ACM International Conference on Multimedia. IEEE Access 8 (2020), 193907--193934. RACE: Large-scale ReAding Comprehension Dataset From Examinations. Daesik Kim, YoungJoon Yoo, Jeesoo Kim, Sangkuk Lee, and Nojun Kwak. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. These datasets cover a wide range of tasks and require di- ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Our multi-task loss consists of four tasks, engineered to align vision and language representations at multiple levels. In the VE task, image is the premise, and text is the hypothesis. 12-in-1: Multi-Task Vision and Language Representation Learning 8. Journalist: Yuan Yuan | Editor: Michael Sarazen. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. Visual Reasoning and Compositional Question Answering (GQA). 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. Ronald W. Ferguson and Kenneth D. Forbus. Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. Are you sure you want to create this branch? . 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.