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Visual question answering is the task of returning the answer to a question about an image.
A coefficient of agreement for nominal scales
Jacob Cohen · 1960
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From covariation to causation: a causal power theory
Patricia W Cheng · 1997
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The meaning and computation of causal power: Comment on cheng (1997) and novick and cheng (2004)
Christian C Luhmann and Woo-kyoung Ahn · 2005
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VizWiz: Nearly real-time answers to visual questions
Jeffrey P Bigham, Chandrika Jayant, Hanjie Ji, Greg Little, Andrew Miller, Robert C Miller, Robin Miller, Aubrey Tatarowicz, Brandyn White, Samual White, and others · 2010
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The multidimensional wisdom of crowds
Peter Welinder, Steve Branson, Pietro Perona, and Serge J Belongie · 2010
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What makes an image memorable?
Phillip Isola, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 2011
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How much spam can you take? an analysis of crowdsourcing results to increase accuracy
Jeroen Vuurens, Arjen P de Vries, and Carsten Eickhoff · 2011
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Crowdsourcing subjective fashion advice using vizwiz: challenges and opportunities
Michele A Burton, Erin Brady, Robin Brewer, Callie Neylan, Jeffrey P Bigham, and Amy Hurst · 2012
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Obtaining high-quality relevance judgments using crowdsourcing
Jeroen BP Vuurens and Arjen P De Vries · 2012
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Crowd Truth: Harnessing disagreement in crowdsourcing a relation extraction gold standard
Lora Aroyo and Chris Welty · 2013
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Visual challenges in the everyday lives of blind people
Erin Brady, Meredith Ringel Morris, Yu Zhong, Samuel White, and Jeffrey P. Bigham · 2013
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Increasing cheat robustness of crowdsourcing tasks
Carsten Eickhoff and Arjen P de Vries · 2013
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Domain-independent quality measures for crowd truth disagreement
Oana Inel, Lora Aroyo, Chris Welty, and Robert-Jan Sips · 2013
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Square: A benchmark for research on computing crowd consensus
Aashish Sheshadri and Matthew Lease · 2013
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Measuring crowd truth: Disagreement metrics combined with worker behavior filters
Guillermo Soberón, Lora Aroyo, Chris Welty, Oana Inel, Hui Lin, and Manfred Overmeen · 2013
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Agreement/disagreement based crowd labeling
Hossein Amirkhani and Mohammad Rahmati · 2014
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Crowdtruth: Machine-human computation framework for harnessing disagreement in gathering annotated data
Oana Inel, Khalid Khamkham, Tatiana Cristea, Anca Dumitrache, Arne Rutjes, Jelle van der Ploeg, Lukasz Romaszko, Lora Aroyo, and Robert-Jan Sips · 2014
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Multiview triplet embedding: Learning attributes in multiple maps
Ehsan Amid and Antti Ukkonen · 2015
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VQA: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
Crowdsourcing ground truth for medical relation extraction
Anca Dumitrache, Lora Aroyo, and Chris Welty · 2017
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Clarity is a worthwhile quality: On the role of task clarity in microtask crowdsourcing
Ujwal Gadiraju, Jie Yang, and Alessandro Bozzon · 2017
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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CrowdVerge: Predicting If People Will Agree on the Answer to a Visual Question
Danna Gurari and Kristen Grauman · 2017
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An analysis of visual question answering algorithms
Kushal Kafle and Christopher Kanan · 2017
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Understanding malicious behavior in crowdsourcing platforms: The case of online surveys
Ujwal Gadiraju, Ricardo Kawase, Stefan Dietze, and Gianluca Demartini · 2015
Cited alongside, same era.
Image specificity
Mainak Jas and Devi Parikh · 2015
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Discovering attribute shades of meaning with the crowd
Adriana Kovashka and Kristen Grauman · 2015
Cited alongside, same era.
Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images
Mateusz Malinowski, Marcus Rohrbach, and Mario Fritz · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Cited alongside, same era.
We are humor beings: Understanding and predicting visual humor
Arjun Chandrasekaran, Ashwin K. Vijayakumar, Stanislaw Antol, Mohit Bansal, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh · 2016
Cited alongside, same era.
Learning to Disambiguate by Asking Discriminative Questions
Yining Li, Chen Huang, Xiaoou Tang, and Chen Change Loy · 2017
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The promise of premise: Harnessing question premises in visual question answering
Aroma Mahendru, Viraj Prabhu, Akrit Mohapatra, Dhruv Batra, and Stefan Lee · 2017
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Crowdsourcing question-answer meaning representations
Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, and Luke Zettlemoyer · 2017
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Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
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Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation (s)
Danna Gurari, Kun He, Bo Xiong, Jianming Zhang, Mehrnoosh Sameki, Suyog Dutt Jain, Stan Sclaroff, Margrit Betke, and Kristen Grauman · 2018
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VizWiz Grand Challenge: Answering Visual Questions from Blind People
Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P. Bigham · 2018
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Tips and tricks for visual question answering: Learnings from the 2017 challenge
Damien Teney, Peter Anderson, Xiaodong He, and Anton van den Hengel · 2018
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Tips and tricks for visual question answering: Learnings from the 2017 challenge
Damien Teney, Peter Anderson, Xiaodong He, and Anton van den Hengel · 2018
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Visual Question Answer Diversity
Chun-Ju Yang, Kristen Grauman, and Danna Gurari · 2018
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