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A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings.
Bleu: a method for automatic evaluation of machine translation
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Chin-Yew Lin. 2004 · 2004
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Understanding inverse document frequency: on theoretical arguments for idf
S. Robertson. 2004 · 2004
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Collecting highly parallel data for paraphrase evaluation
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Image description using visual dependency representations
Desmond Elliott and Frank Keller. 2013 · 2013
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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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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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Cited alongside, same era.
Msr-vtt: A large video description dataset for bridging video and language
Re-evaluating automatic metrics for image captioning
Mert Kilickaya, Aykut Erdem, Nazli Ikizler-Cinbis, and Erkut Erdem. 2017 · 2017
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Shikhar Sharma, Layla El Asri, Hannes Schulz, and Jeremie Zumer. 2017 · 2017
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No metrics are perfect: Adversarial reward learning for visual storytelling
Xin Wang, Wenhu Chen, Yuan-Fang Wang, and William Yang Wang. 2018 · 2018
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Vatex: A large-scale, high-quality multilingual dataset for video-and-language research
Xin Wang, Jiawei Wu, Junkun Chen, Lei Li, Yuan-Fang Wang, and William Yang Wang. 2019 · 2019
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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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Jun Xu, Tao Mei, Ting Yao, and Yong Rui. 2016 · 2016
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015 · 2057
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