Fetching the paper…
Reading the bibliography…
In this work we present DREAM, an fMRI-to-image method for reconstructing viewed images from brain activities, grounded on fundamental knowledge of the human visual system.
The central nervous system: structure and function
Per Brodal · 2004
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Measurement of the relationship between perceived and computed color differences
Pedro A Garcia, Rafael Huertas, Manuel Melgosa, and Guihua Cui · 2007
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.
Brain-computer interfaces and human-computer interaction
Desney Tan and Anton Nijholt · 2010
Earlier work this paper cites.
Pixels to voxels: modeling visual representation in the human brain
Pulkit Agrawal, Dustin Stansbury, Jitendra Malik, and Jack L Gallant · 2014
Earlier work this paper cites.
Microsoft
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.
Holistically-nested edge detection
Saining Xie and Zhuowen Tu · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Generic decoding of seen and imagined objects using hierarchical visual features
Tomoyasu Horikawa and Yukiyasu Kamitani · 2017
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
Earlier work this paper cites.
Color consistency correction based on remapping optimization for image stitching
Menghan Xia, Jian Yao, Renping Xie, Mi Zhang, and Jinsheng Xiao · 2017
Earlier work this paper cites.
Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018
Earlier work this paper cites.
Deep image reconstruction from human brain activity
Guohua Shen, Tomoyasu Horikawa, Kei Majima, and Yukiyasu Kamitani · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Earlier work this paper cites.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Earlier work this paper cites.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Earlier work this paper cites.
Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Mixco: Mix-up contrastive learning for visual representation
Sungnyun Kim, Gihun Lee, Sangmin Bae, and Se-Young Yun · 2020
Cited alongside, same era.
Reconstructing natural scenes from fmri patterns using bigbigan
Milad Mozafari, Leila Reddy, and Rufin VanRullen · 2020
Cited alongside, same era.
Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun · 2020
Cited alongside, same era.
Very deep vaes generalize autoregressive models and can outperform them on images
Rewon Child · 2021
Cited alongside, same era.
More than meets the eye: Self-supervised depth reconstruction from brain activity
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Later among the works it cites.
Palgan: Image colorization with palette generative adversarial networks
Yi Wang, Menghan Xia, Lu Qi, Jing Shao, and Yu Qiao · 2022
Later among the works it cites.
Versatile diffusion: Text, images and variations all in one diffusion model
Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, and Humphrey Shi · 2022
Later among the works it cites.
Balanced contrastive learning for long-tailed visual recognition
Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi-Ping Phoebe Chen, and Yu-Gang Jiang · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Guy Gaziv and Michal Irani · 2021
Cited alongside, same era.
fmri-based decoding of visual information from human brain activity: A brief review
Shuo Huang, Wei Shao, Mei-Ling Wang, and Dao-Qiang Zhang · 2021
Cited alongside, same era.
Self-damaging contrastive learning
Ziyu Jiang, Tianlong Chen, Bobak J Mortazavi, and Zhangyang Wang · 2021
Cited alongside, same era.
Semantic palette: Guiding scene generation with class proportions
Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain, Patrick Pérez, and Matthieu Cord · 2021
Cited alongside, same era.
Deep learning for monocular depth estimation: A review
Yue Ming, Xuyang Meng, Chunxiao Fan, and Hui Yu · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Pixel difference networks for efficient edge detection
Zhuo Su, Wenzhe Liu, Zitong Yu, Dewen Hu, Qing Liao, Qi Tian, Matti Pietikäinen, and Li Liu · 2021
Cited alongside, same era.
Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding
Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou · 2023
Closest in time.
Brain Captioning: Decoding human brain activity into images and text
Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, and Nicola Toschi · 2023
Closest in time.
Decoding natural image stimuli from fmri data with a surface-based convolutional network
Zijin Gu, Keith Jamison, Amy Kuceyeski, and Mert Sabuncu · 2023
Closest in time.
Yulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, and Nanning Zheng · 2023
Closest in time.
Yizhuo Lu, Changde Du, Dianpeng Wang, and Huiguang He · 2023
Closest in time.
Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie · 2023
Closest in time.
Brain-Diffuser: Natural scene reconstruction from fMRI signals using generative latent diffusion
Furkan Ozcelik and Rufin VanRullen · 2023
Closest in time.
Reconstructing the mind’s eye: fmri-to-image with contrastive learning and diffusion priors
Paul S Scotti, Atmadeep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Ethan Cohen, Aidan J Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, et al · 2023
Closest in time.
High-resolution image reconstruction with latent diffusion models from human brain activity
Yu Takagi and Shinji Nishimoto · 2023
Closest in time.
Yu Takagi and Shinji Nishimoto · 2023
Closest in time.
Controllable mind visual diffusion model
Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao, Xiaolong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, and Baochang Zhang · 2023
Closest in time.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang and Maneesh Agrawala · 2023
Closest in time.