M
Margaret Mitchell
Researcher at Google
Publications - 109
Citations - 18187
Margaret Mitchell is an academic researcher from Google. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 42, co-authored 94 publications receiving 13094 citations. Previous affiliations of Margaret Mitchell include University of Aberdeen & Johns Hopkins University.
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Proceedings ArticleDOI
VQA: Visual Question Answering
Stanislaw Antol,Aishwarya Agrawal,Jiasen Lu,Margaret Mitchell,Dhruv Batra,C. Lawrence Zitnick,Devi Parikh +6 more
TL;DR: The task of free-form and open-ended Visual Question Answering (VQA) is proposed, given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
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VQA: Visual Question Answering
Aishwarya Agrawal,Jiasen Lu,Stanislaw Antol,Margaret Mitchell,C. Lawrence Zitnick,Dhruv Batra,Devi Parikh +6 more
TL;DR: The task of free-form and open-ended Visual Question Answering (VQA) is proposed, given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
Proceedings ArticleDOI
From captions to visual concepts and back
Hao Fang,Saurabh Gupta,Forrest Iandola,Rupesh Kumar Srivastava,Li Deng,Piotr Dollár,Jianfeng Gao,Xiaodong He,Margaret Mitchell,John Platt,C. Lawrence Zitnick,Geoffrey Zweig +11 more
TL;DR: This paper used multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives, which serve as conditional inputs to a maximum-entropy language model.
Proceedings ArticleDOI
Mitigating Unwanted Biases with Adversarial Learning
TL;DR: This work presents a framework for mitigating biases concerning demographic groups by including a variable for the group of interest and simultaneously learning a predictor and an adversary, which results in accurate predictions that exhibit less evidence of stereotyping Z.
Proceedings ArticleDOI
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
Alessandro Sordoni,Michel Galley,Michael Auli,Chris Brockett,Yangfeng Ji,Margaret Mitchell,Jian-Yun Nie,Jianfeng Gao,Bill Dolan +8 more
TL;DR: A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances.