Fetching the paper…
Reading the bibliography…
Computer vision systems today are primarily N-purpose systems, designed and trained for a predefined set of tasks.
ImageNet: A large-scale hierarchical image database
Wei Dong, Richard Socher, Li Li-Jia, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Every picture tells a story: Generating sentences from images
Ali Farhadi, Seyyed Hejrati, Amin Sadeghi, Peter Young, Cyrus Rashtchian, Julia Hockenmaier, and David A. Forsyth · 2010
Earlier work this paper cites.
Baby talk: Understanding and generating image descriptions
Girish Kulkarni, Visruth Premraj, Sagnik Dhar, Siming Li, Yejin Choi, Alexander C Berg, and Tamara L Berg · 2011
Earlier work this paper cites.
ReferItGame: Referring to objects in photographs of natural scenes
Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg · 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.
VQA: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 2015
Earlier work this paper cites.
Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan Plummer, Liwei Wang, Chris Cervantes, Juan Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C. L. Zitnick, and D. Parikh · 2015
Earlier work this paper cites.
Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan · 2015
Earlier work this paper cites.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio · 2015
Earlier work this paper cites.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deep compositional captioning: Describing novel object categories without paired training data
Lisa Anne Hendricks, Subhashini Venugopalan, Marcus Rohrbach, Raymond J. Mooney, Kate Saenko, and Trevor Darrell · 2016
Earlier work this paper cites.
Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher · 2016
Earlier work this paper cites.
Generation and comprehension of unambiguous object descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy · 2016
Earlier work this paper cites.
Grounding of textual phrases in images by reconstruction
Anna Rohrbach, Marcus Rohrbach, Ronghang Hu, Trevor Darrell, and Bernt Schiele · 2016
Earlier work this paper cites.
Ask me anything: Free-form visual question answering based on knowledge from external sources
Qi Wu, Peng Wang, Chunhua Shen, Anthony Dick, and Anton van den Hengel · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
Earlier work this paper cites.
Visual dialog
Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José Moura, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
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
Cited alongside, same era.
Aligned image-word representations improve inductive transfer across vision-language tasks
Tanmay Gupta, Kevin J. Shih, Saurabh Singh, and Derek Hoiem · 2017
Cited alongside, same era.
Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick · 2017
Cited alongside, same era.
Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, Michael Bernstein, and Li Fei-Fei · 2017
Cited alongside, same era.
Phrase localization and visual relationship detection with comprehensive image-language cues
Bryan Plummer, Arun Mallya, Christopher Cervantes, Julia Hockenmaier, and Svetlana Lazebnik · 2017
Cited alongside, same era.
Unifiedqa: Crossing format boundaries with a single qa system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, P. Clark, and Hannaneh Hajishirzi · 2020
Later among the works it cites.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
Later among the works it cites.
12-in-1: Multi-task vision and language representation learning
Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, D. Parikh, and Stefan Lee · 2020
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, W. Li, and Peter J. Liu · 2020
Later among the works it cites.
Green ai
Roy Schwartz, Jesse Dodge, Noah Smith, and Oren Etzioni · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
Cited alongside, same era.
Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, X. He, C. Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
Cited alongside, same era.
Neural baby talk
Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh · 2018
Cited alongside, same era.
The natural language decathlon: Multitask learning as question answering
B. McCann, N. Keskar, Caiming Xiong, and R. Socher · 2018
Cited alongside, same era.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
Cited alongside, same era.
FVQA: Fact-based visual question answering
Peng Wang, Qi Wu, Chunhua Shen, Anthony Dick, and Anton van den Hengel · 2018
Cited alongside, same era.
Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew E. Peters · 2020
Later among the works it cites.
Unified vision-language pre-training for image captioning and vqa
Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J. Corso, and Jianfeng Gao · 2020
Later among the works it cites.
Unifying vision-and-language tasks via text generation
Jaemin Cho, Jie Lei, Haochen Tan, and Mohit Bansal · 2021
Closest in time.
Beyond question-based biases: Assessing multimodal shortcut learning in visual question answering
Corentin Dancette, Rémi Cadène, Damien Teney, and Matthieu Cord · 2021
Closest in time.
Perceiver io: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Andrew Brock, Evan Shelhamer, Olivier J. H’enaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, and João Carreira · 2021
Closest in time.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, and João Carreira · 2021
Closest in time.
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, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Closest in time.
Mdetr - modulated detection for end-to-end multi-modal understanding
Aishwarya Kamath, Mannat Singh, Yann LeCun, Ishan Misra, Gabriel Synnaeve, and Nicolas Carion · 2021
Closest in time.
Less is more: Clipbert for video-and-language learning via sparse sampling
Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, and Jingjing Liu · 2021
Closest in time.
Learning transferable visual models from natural language supervision, 2021
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
Closest in time.
Separating skills and concepts for novel visual question answering
Spencer Whitehead, Hui Wu, Heng Ji, Rogério Schmidt Feris, Kate Saenko, and Uiuc MIT-IBM · 2021
Closest in time.
E2e-vlp: End-to-end vision-language pre-training enhanced by visual learning
Haiyang Xu, Ming Yan, Chenliang Li, Bin Bi, Songfang Huang, Wenming Xiao, and Fei Huang · 2021
Closest in time.
Merlot: Multimodal neural script knowledge models
Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi · 2021
Closest in time.
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
Closest in time.
Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hanna Hajishirzi · 2022
Closest in time.