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Image captioning is an important task for benchmarking visual reasoning and for enabling accessibility for people with vision impairments.
College admissions and the stability of marriage
David Gale and Lloyd S Shapley · 1962
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Microsoft COCO captions: Data collection and evaluation server
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Unequal representation and gender stereotypes in image search results for occupations
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Adam: A method for stochastic optimization
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Show and tell: A neural image caption generator
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Stereotyping and bias in the Flickr30K dataset
Emiel van Miltenburg · 2016
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Image captioning with semantic attention
Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo · 2016
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The problem with bias: Allocative versus representational harms in machine learning
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Semantics derived automatically from language corpora contain human-like biases
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Knowing when to look: Adaptive attention via a visual sentinel for image captioning
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Understanding blind people’s experiences with computer-generated captions of social media images
Haley MacLeod, Cynthia L Bennett, Meredith Ringel Morris, and Edward Cutrell · 2017
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Self-critical sequence training for image captioning
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
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Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez · 2019
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Predictive inequity in object detection
Benjamin Wilson, Judy Hoffman, and Jamie Morgenstern · 2019
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Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach · 2020
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Towards a critical race methodology in algorithmic fairness
Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smith-Loud · 2020
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Bottom-up and top-down attention for image captioning and visual question answering
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”Person, shoes, tree. is the person naked?” What people with vision impairments want in image descriptions
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REVISE: A tool for measuring and mitigating bias in visual datasets
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Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the ImageNet hierarchy
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“It’s complicated”: Negotiating accessibility and (mis)representation in image descriptions of race, gender, and disability
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