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Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Confidence estimation for machine translation
John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. 2004 · 2004
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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METEOR: an automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
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Machine translation evaluation versus quality estimation
Lucia Specia, Dhwaj Raj, and Marco Turchi. 2010 · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
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Understanding and predicting importance in images
Alexander C. Berg, Tamara L. Berg, Hal Daumé III, Jesse Dodge, Amit Goyal, Xufeng Han, Alyssa Mensch, Margaret Mitchell, Aneesh Sood, Karl Stratos, and Kota Yamaguchi. 2012 · 2012
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Match without a referee: evaluating mt adequacy without reference translations
Yashar Mehdad, Matteo Negri, and Marcello Federico. 2012 · 2012
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Framing image description as a ranking task: Data, models and evaluation metrics
Micah Hodosh, Peter Young, and Julia Hockenmaier. 2013 · 2013
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Automatically assessing machine summary content without a gold standard
Annie Louis and Ani Nenkova. 2013 · 2013
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Comparing automatic evaluation measures for image description
Desmond Elliott and Frank Keller. 2014 · 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 · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
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From images to sentences through scene description graphs using commonsense reasoning and knowledge
Somak Aditya, Yezhou Yang, Chitta Baral, Cornelia Fermuller, and Yiannis Aloimonos. 2015 · 2015
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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Show and tell: Lessons learned from the 2015 mscoco image captioning challenge
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2016 · 2015
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Spice: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn. 2016 · 2016
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Bringing semantics into focus using visual abstraction
C Lawrence Zitnick and Devi Parikh. 2013 · 2016
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Spice: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2016 · 2016
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Contrastive learning for image captioning
Bo Dai and Dahua Lin. 2017 · 2017
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Re-evaluating automatic metrics for image captioning
Mert Kilickaya, Aykut Erdem, Nazli Ikizler-Cinbis, and Erkut Erdem. 2017 · 2017
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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 · 2017
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Movie description
Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. 2017 · 2017
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FOIL it! find one mismatch between image and language caption
Ravi Shekhar, Sandro Pezzelle, Yauhen Klimovich, Aurélie Herbelot, Moin Nabi, Enver Sangineto, and Raffaella Bernardi. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Learning to evaluate image captioning
Yin Cui, Guandao Yang, Andreas Veit, Xun Huang, and Serge Belongie. 2018 · 2018
Cited alongside, same era.
Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 2018 · 2018
Cited alongside, same era.
Stacked cross attention for image-text matching
Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. 2018 · 2018
Cited alongside, same era.
Show, tell and discriminate: Image captioning by self-retrieval with partially labeled data
The feasibility of embedding based automatic evaluation for single document summarization
Simeng Sun and Ani Nenkova. 2019 · 2019
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Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT
Shijie Wu and Mark Dredze. 2019 · 2019
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Quality estimation and translation metrics via pre-trained word and sentence embeddings
Elizaveta Yankovskaya, Andre Tättar, and Mark Fishel. 2019 · 2019
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Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020 · 2020
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Twitter a11y: A browser extension to make twitter images accessible
Cole Gleason, Amy Pavel, Emma McCamey, Christina Low, Patrick Carrington, Kris M Kitani, and Jeffrey P Bigham. 2020 · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Xihui Liu, Hongsheng Li, Jing Shao, Dapeng Chen, and Xiaogang Wang. 2018 · 2018
Cited alongside, same era.
Discriminability objective for training descriptive captions
Ruotian Luo, Brian Price, Scott Cohen, and Gregory Shakhnarovich. 2018 · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Cited alongside, same era.
Objective function learning to match human judgements for optimization-based summarization
Maxime Peyrard and Iryna Gurevych. 2018 · 2018
Cited alongside, same era.
Object hallucination in image captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Cited alongside, same era.
Machine translation quality estimation: Applications and future perspectives
Lucia Specia and Kashif Shah. 2018 · 2018
Cited alongside, same era.
Ruber: An unsupervised method for automatic evaluation of open-domain dialog systems
Chongyang Tao, Lili Mou, Dongyan Zhao, and Rui Yan. 2018 · 2018
Cited alongside, same era.
Karthikeyan K, Zihan Wang, Stephen Mayhew, and Dan Roth. 2020 · 2020
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NUBIA: NeUral based interchangeability assessor for text generation
Hassan Kane, Muhammed Yusuf Kocyigit, Ali Abdalla, Pelkins Ajanoh, and Mohamed Coulibali. 2020 · 2020
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Vilbertscore: Evaluating image caption using vision-and-language bert
Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, and Kyomin Jung. 2020 · 2020
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12-in-1: Multi-task vision and language representation learning
Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, and Stefan Lee. 2020 · 2020
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USR: An unsupervised and reference free evaluation metric for dialog generation
Shikib Mehri and Maxine Eskenazi. 2020 · 2020
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“person, shoes, tree. is the person naked?" what people with vision impairments want in image descriptions
Abigale Stangl, Meredith Ringel Morris, and Danna Gurari. 2020 · 2020
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Improving image captioning evaluation by considering inter references variance
Yanzhi Yi, Hangyu Deng, and Jinglu Hu. 2020 · 2020
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BERTScore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2020 · 2020
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On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation
Wei Zhao, Goran Glavaš, Maxime Peyrard, Yang Gao, Robert West, and Steffen Eger. 2020 · 2020
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With little power comes great responsibility
Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, and Dan Jurafsky. 2020 · 2020
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NUBIA: NeUral based interchangeability assessor for text generation
Hassan Kane, Muhammed Yusuf Kocyigit, Ali Abdalla, Pelkins Ajanoh, and Mohamed Coulibali. 2020 · 2020
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BERTScore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2020 · 2020
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Evaluating CLIP: Towards characterization of broader capabilities and downstream implications
Sandhini Agarwal, Gretchen Krueger, Jack Clark, Alec Radford, Jong Wook Kim, and Miles Brundage. 2021 · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V Le, Yunhsuan Sung, Zhen Li, and Tom Duerig. 2021 · 2021
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UMIC: an unreferenced metric for image captioning via contrastive learning
Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Trung Bui, and Kyomin Jung. 2021 · 2021
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NewsCLIPpings: automatic generation of out-of-context multimodal media
Grace Luo, Trevor Darrell, and Anna Rohrbach. 2021 · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
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FAIEr: Fidelity and adequacy ensured image caption evaluation
Sijin Wang, Ziwei Yao, Ruiping Wang, Zhongqin Wu, and Xilin Chen. 2021 · 2021
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