Fetching the paper…
Reading the bibliography…
While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major barrier to using these systems in the real world.
Bimodal distribution removal
P Slade and Tamás D Gedeon · 1993
Earlier work this paper cites.
Approximation algorithms
Vijay V Vazirani · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
Earlier work this paper cites.
Meteor, m-bleu and m-ter: Evaluation metrics for high-correlation with human rankings of machine translation output
Abhaya Agarwal and Alon Lavie · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning from multi-label data
Grigorios Tsoumakas and Min-Ling Zhang · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Collecting highly parallel data for paraphrase evaluation
David Chen and William B Dolan · 2011
Earlier work this paper cites.
Collecting highly parallel data for paraphrase evaluation
David Chen and William B Dolan · 2011
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Earlier work this paper cites.
Msr-vtt: A large video description dataset for bridging video and language
Jun Xu, Tao Mei, Ting Yao, and Yong Rui · 2016
Earlier work this paper cites.
Video summarization with long short-term memory
Ke Zhang, Wei-Lun Chao, Fei Sha, and Kristen Grauman · 2016
Earlier work this paper cites.
Spice: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould · 2016
Earlier work this paper cites.
Msr-vtt: A large video description dataset for bridging video and language
Jun Xu, Tao Mei, Ting Yao, and Yong Rui · 2016
Earlier work this paper cites.
Dense-captioning events in videos
Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Earlier work this paper cites.
Self-critical sequence training for image captioning
Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel · 2017
Cited alongside, same era.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Cited alongside, same era.
Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson et al · 2018
Cited alongside, same era.
A short note about kinetics-600
Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman · 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
Cited alongside, same era.
Gender slopes: Counterfactual fairness for computer vision models by attribute manipulation
Jungseock Joo and Kimmo Kärkkäinen · 2020
Later among the works it cites.
Don’t judge an object by its context: Learning to overcome contextual bias
Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, and Deepti Ghadiyaram · 2020
Later among the works it cites.
Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu · 2020
Later among the works it cites.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
Later among the works it cites.
Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
Cited alongside, same era.
Towards automatic learning of procedures from web instructional videos
Luowei Zhou, Chenliang Xu, and Jason J Corso · 2018
Cited alongside, same era.
A short note about kinetics-600
Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman · 2018
Cited alongside, same era.
Video description: A survey of methods, datasets, and evaluation metrics
Nayyer Aafaq, Ajmal Mian, Wei Liu, Syed Zulqarnain Gilani, and Mubarak Shah · 2019
Cited alongside, same era.
Rehumanized crowdsourcing: a labeling framework addressing bias and ethics in machine learning
Natã M Barbosa and Monchu Chen · 2019
Cited alongside, same era.
Exposing and correcting the gender bias in image captioning datasets and models
Shruti Bhargava and David Forsyth · 2019
Cited alongside, same era.
Rubi: Reducing unimodal biases for visual question answering
Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al · 2019
Cited alongside, same era.
Later among the works it cites.
Bertscore: Evaluating text generation with bert
Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi · 2020
Later among the works it cites.
Object relational graph with teacher-recommended learning for video captioning
Ziqi Zhang, Yaya Shi, Chunfeng Yuan, Bing Li, Peijin Wang, Weiming Hu, and Zheng-Jun Zha · 2020
Later among the works it cites.
Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu · 2020
Later among the works it cites.
Motion guided region message passing for video captioning
Shaoxiang Chen and Yu-Gang Jiang · 2021
Later among the works it cites.
Smurf: Semantic and linguistic understanding fusion for caption evaluation via typicality analysis
Joshua Feinglass and Yezhou Yang · 2021
Later among the works it cites.
O2na: An object-oriented non-autoregressive approach for controllable video captioning
Fenglin Liu, Xuancheng Ren, Xian Wu, Bang Yang, Shen Ge, and Xu Sun · 2021
Later among the works it cites.
Spoken moments: Learning joint audio-visual representations from video descriptions
Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, and Aude Oliva · 2021
Later among the works it cites.
Improving video captioning with temporal composition of a visual-syntactic embedding
Jesus Perez-Martin, Benjamin Bustos, and Jorge Pérez · 2021
Later among the works it cites.
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, et al · 2021
Later among the works it cites.
Worst of both worlds: Biases compound in pre-trained vision-and-language models
Tejas Srinivasan and Yonatan Bisk · 2021
Later among the works it cites.
From show to tell: A survey on image captioning
Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Silvia Cascianelli, Giuseppe Fiameni, and Rita Cucchiara · 2021
Later among the works it cites.
Mitigating gender bias in captioning systems
Ruixiang Tang, Mengnan Du, Yuening Li, Zirui Liu, Na Zou, and Xia Hu · 2021
Later among the works it cites.
Faier: Fidelity and adequacy ensured image caption evaluation
Sijin Wang, Ziwei Yao, Ruiping Wang, Zhongqin Wu, and Xilin Chen · 2021
Later among the works it cites.
Non-autoregressive coarse-to-fine video captioning
Bang Yang, Yuexian Zou, Fenglin Liu, and Can Zhang · 2021
Later among the works it cites.
Deconfounded image captioning: A causal retrospect
Xu Yang, Hanwang Zhang, and Jianfei Cai · 2021
Later among the works it cites.
Vinvl: Revisiting visual representations in vision-language models
Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao · 2021
Later among the works it cites.
Understanding and evaluating racial biases in image captioning
Dora Zhao, Angelina Wang, and Olga Russakovsky · 2021
Later among the works it cites.