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We propose a new paradigm to automatically generate training data with accurate labels at scale using the text-to-image synthesis frameworks (e.g., DALL-E, Stable Diffusion, etc.).
An empirical study of context in object detection
Santosh K Divvala, Derek Hoiem, James H Hays, Alexei A Efros, and Martial Hebert · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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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
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The role of context for object detection and semantic segmentation in the wild
Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille · 2014
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
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The ycb object and model set: Towards common benchmarks for manipulation research
Berk Calli, Arjun Singh, Aaron Walsman, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar · 2015
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Scenenet: Understanding real world indoor scenes with synthetic data
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, and Roberto Cipolla · 2015
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Render for CNN: viewpoint estimation in images using cnns trained with rendered 3d model views
Hao Su, Charles Ruizhongtai Qi, Yangyan Li, and Leonidas J. Guibas · 2015
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Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan · 2015
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Multiview rgb-d dataset for object instance detection
Georgios Georgakis, Md Alimoor Reza, Arsalan Mousavian, Phi-Hung Le, and Jana Košecká · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
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The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes
Germán Ros, Laura Sellart, Joanna Materzynska, David Vázquez, and Antonio M. López · 2016
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A dataset for developing and benchmarking active vision
Phil Ammirato, Patrick Poirson, Eunbyung Park, Jana Košecká, and Alexander C Berg · 2017
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
Debidatta Dwibedi, Ishan Misra, and Martial Hebert · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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On pre-trained image features and synthetic images for deep learning
Stefan Hinterstoisser, Vincent Lepetit, Paul Wohlhart, and Kurt Konolige · 2017
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Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth
Mahdi Rad and Vincent Lepetit · 2017
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Faster r-cnn: towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2017
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Self-critical sequence training for image captioning
Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel · 2017
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Playing for benchmarks
Stephan R. Richter, Zeeshan Hayder, and Vladlen Koltun · 2017
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Fully convolutional networks for semantic segmentation
Evan Shelhamer, Jonathan Long, and Trevor Darrell · 2017
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Semantic scene completion from a single depth image
Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser · 2017
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Real-time seamless single shot 6d object pose prediction
Bugra Tekin, Sudipta N. Sinha, and Pascal Fua · 2017
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Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes
Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox · 2017
Cited alongside, same era.
Don’t just assume; look and answer: Overcoming priors for visual question answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi · 2018
Cited alongside, same era.
Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
Cited alongside, same era.
Gan augmentation: Augmenting training data using generative adversarial networks
Simple copy-paste is a strong data augmentation method for instance segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph · 2021
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Zero-shot detection via vision and language knowledge distillation
Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui · 2021
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Mdetr-modulated detection for end-to-end multi-modal understanding
Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion · 2021
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Open-world entity segmentation
Lu Qi, Jason Kuen, Yi Wang, Jiuxiang Gu, Hengshuang Zhao, Zhe Lin, Philip Torr, and Jiaya Jia · 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, et al · 2021
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Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdés Hernández, Joanna Wardlaw, and Daniel Rueckert · 2018
Cited alongside, same era.
Embodied question answering
Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra · 2018
Cited alongside, same era.
Modeling visual context is key to augmenting object detection datasets
Nikita Dvornik, Julien Mairal, and Cordelia Schmid · 2018
Cited alongside, same era.
Context-aware synthesis and placement of object instances
Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, and Jan Kautz · 2018
Cited alongside, same era.
Falling things: A synthetic dataset for 3d object detection and pose estimation
J. Tremblay, T. To, and S. Birchfield · 2018
Cited alongside, same era.
Falling things: A synthetic dataset for 3d object detection and pose estimation
Jonathan Tremblay, Thang To, and Stan Birchfield · 2018
Cited alongside, same era.
Photorealistic image synthesis for object instance detection
Tomáš Hodaň, Vibhav Vineet, Ran Gal, Emanuel Shalev, Jon Hanzelka, Treb Connell, Pedro Urbina, Sudipta Sinha, and Brian Guenter · 2019
Cited alongside, same era.
Learning to generate synthetic data via compositing
Shashank Tripathi, Siddhartha Chandra, Amit Agrawal, Ambrish Tyagi, James M Rehg, and Visesh Chari · 2019
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Causal attention for vision-language tasks
Xu Yang, Hanwang Zhang, Guojun Qi, and Jianfei Cai · 2021
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Cut-and-paste dataset generation for balancing domain gaps in object instance detection
Woo-Han Yun, Taewoo Kim, Jaeyeon Lee, Jaehong Kim, and Junmo Kim · 2021
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https://github.com/sberbank-ai/ru-dalle
sberbank-ai/ru-dalle: Generate images from texts. in russian · 2022
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ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers, 2022
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu · 2022
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Pp-matting: High-accuracy natural image matting
Guowei Chen, Yi Liu, Jian Wang, Juncai Peng, Yuying Hao, Lutao Chu, Shiyu Tang, Zewu Wu, Zeyu Chen, Zhiliang Yu, et al · 2022
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Neural-sim: Learning to generate training data with nerf
Yunhao Ge, Harkirat Behl, Jiashu Xu, Suriya Gunasekar, Neel Joshi, Yale Song, Xin Wang, Laurent Itti, and Vibhav Vineet · 2022
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Dall-e for detection: Language-driven compositional image synthesis for object detection
Yunhao Ge, Jiashu Xu, Brian Nlong Zhao, Neel Joshi, Laurent Itti, and Vibhav Vineet · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Language-driven semantic segmentation
Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and René Ranftl · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Grounded language-image pre-training
Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo-Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
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X-paste: Revisit copy-paste at scale with clip and stablediffusion
Hanqing Zhao, Dianmo Sheng, Jianmin Bao, Dongdong Chen, Dong Chen, Fang Wen, Lu Yuan, Ce Liu, Wenbo Zhou, Qi Chu, et al · 2022
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Muse: Text-to-image generation via masked generative transformers, 2023
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, and Dilip Krishnan · 2023
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Eva: Exploring the limits of masked visual representation learning at scale
Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, and Yue Cao · 2023
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Your diffusion model is secretly a zero-shot classifier
Alexander C Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak · 2023
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Gpt-4 technical report
OpenAI · 2023
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