S
Stanislaw Antol
Researcher at Virginia Tech
Publications - 15
Citations - 6383
Stanislaw Antol is an academic researcher from Virginia Tech. The author has contributed to research in topics: Question answering & Context (language use). The author has an hindex of 8, co-authored 15 publications receiving 4944 citations. Previous affiliations of Stanislaw Antol include Samsung.
Papers
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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.
Posted Content
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.
Journal ArticleDOI
VQA: Visual Question Answering
Aishwarya Agrawal,Jiasen Lu,Stanislaw Antol,Margaret Mitchell,C. Lawrence Zitnick,Devi Parikh,Dhruv Batra +6 more
TL;DR: This article proposed the task of free-form and open-ended Visual Question Answering (VQA), where given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
Book ChapterDOI
Zero-Shot Learning via Visual Abstraction
TL;DR: This paper proposes a new modality for ZSL using visual abstraction to learn difficult-to-describe concepts related to people and their interactions with others, and learns an explicit mapping between the abstract and real worlds.
Journal ArticleDOI
Measuring Machine Intelligence Through Visual Question Answering
C. Lawrence Zitnick,Aishwarya Agrawal,Stanislaw Antol,Margaret Mitchell,Dhruv Batra,Devi Parikh +5 more
TL;DR: In this article, a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence is presented, with a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images.