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Visual reasoning requires multimodal perception and commonsense cognition of the world.
Programs with common sense
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
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Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Joshua B. Tenenbaum · 2018
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Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2018
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 2019
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The neuro-symbolic concept learner: Interpreting scenes words and sentences from natural supervision
Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Rajani, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning · 2019
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Nils Reimers and Iryna Gurevych · 2019
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Hao Tan and Mohit Bansal · 2019
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Ning Xie, Farley Lai, Derek Doran, and Asim Kadav · 2019
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Clevrer: Collision events for video representation and reasoning
Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B. Tenenbaum · 2019
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Deep modular co-attention networks for visual question answering
Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian · 2019
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Neuro-symbolic visual reasoning: Disentangling
Saeed Amizadeh, Hamid Palangi, Alex Polozov, Yichen Huang, and Kazuhito Koishida · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Tydi qa: A benchmark for information-seeking question answering in typologically diverse languages
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e-snli-ve: Corrected visual-textual entailment with natural language explanations
Virginie Do, Oana-Maria Camburu, Zeynep Akata, and Thomas Lukasiewicz · 2020
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2020
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Visualcomet: Reasoning about the dynamic context of a still image
Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, and Yejin Choi · 2020
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Open-retrieval conversational question answering
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Fangyu Liu, Guy Emerson, and Nigel Collier · 2022
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin Raffel · 2022
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Deep learning methods for abstract visual reasoning: A survey on raven’s progressive matrices
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Evaluating large language models trained on code
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A diagnostic study of visual question answering with analogical reasoning
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e-vil: A dataset and benchmark for natural language explanations in vision-language tasks
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A review of emerging research directions in abstract visual reasoning
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Human-level play in the game of diplomacy by combining language models with strategic reasoning
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Training language models to follow instructions with human feedback
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Harnessing the power of multi-task pretraining for ground-truth level natural language explanations
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A-okvqa: A benchmark for visual question answering using world knowledge
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Gamr: A guided attention model for (visual) reasoning
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Rationale-augmented ensembles in language models
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Self-consistency improves chain of thought reasoning in language models
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Chain of thought prompting elicits reasoning in large language models
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
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An empirical study of gpt-3 for few-shot knowledge-based vqa
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Conversational question answering: A survey
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Vqa and visual reasoning: An overview of recent datasets, methods and challenges
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