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Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models.
Seung, H.S., Lee, D.D.: The manifold ways of perception. science 290
2000
Earlier work this paper cites.
Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293
2001
Earlier work this paper cites.
Cox, D.D., Savoy, R.L.: Functional magnetic resonance imaging (fmri)“brain reading”: detecting and classifying distributed patterns of fmri activity in human visual cortex. Neuroimage 19
2003
Earlier work this paper cites.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13
2004
Earlier work this paper cites.
Thirion, B., Duchesnay, E., Hubbard, E., Dubois, J., Poline, J.B., Lebihan, D., Dehaene, S.: Inverse retinotopy: inferring the visual content of images from brain activation patterns. Neuroimage 33
2006
Earlier work this paper cites.
Kay, K.N., Naselaris, T., Prenger, R.J., Gallant, J.L.: Identifying natural images from human brain activity. Nature 452
2008
Earlier work this paper cites.
Glover, G.H.: Overview of functional magnetic resonance imaging. Neurosurgery Clinics 22
2011
Earlier work this paper cites.
Schoenmakers, S., Barth, M., Heskes, T., Van Gerven, M.: Linear reconstruction of perceived images from human brain activity. NeuroImage 83
2013
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Miller, K.L., Alfaro-Almagro, F., Bangerter, N.K., Thomas, D.L., Yacoub, E., Xu, J., Bartsch, A.J., Jbabdi, S., Sotiropoulos, S.N., Andersson, J.L., et al.: Multimodal population brain imaging in the uk biobank prospective epidemiological study. Nature neuroscience 19
2016
Earlier work this paper cites.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2818–2826 (2016)
2016
Earlier work this paper cites.
Chang, N., Pyles, J.A., Marcus, A., Gupta, A., Tarr, M.J., Aminoff, E.M.: Bold5000, a public fmri dataset while viewing 5000 visual images. Scientific data 6
2019
Earlier work this paper cites.
Shen, G., Horikawa, T., Majima, K., Kamitani, Y.: Deep image reconstruction from human brain activity. PLoS computational biology 15
2019
Earlier work this paper cites.
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019)
2019
Earlier work this paper cites.
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Mozafari, M., Reddy, L., VanRullen, R.: Reconstructing natural scenes from fmri patterns using bigbigan. In: 2020 International Joint Conference on Neural Networks, IJCNN 2020, Glasgow, United Kingdom, July 19-24, 2020. pp. 1–8. IEEE (2020)
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021. pp. 12873–12883. Computer Vision Foundation / IEEE (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Cited alongside, same era.
Ren, Z., Li, J., Xue, X., Li, X., Yang, F., Jiao, Z., Gao, X.: Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning. NeuroImage 228
2021
2023
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2023
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2023
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2023
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Cited alongside, same era.
Allen, E.J., St-Yves, G., Wu, Y., Breedlove, J.L., Prince, J.S., Dowdle, L.T., Nau, M., Caron, B., Pestilli, F., Charest, I., et al.: A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence. Nature neuroscience 25
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Gu, Z., Jamison, K.W., Khosla, M., Allen, E.J., Wu, Y., St-Yves, G., Naselaris, T., Kay, K., Sabuncu, M.R., Kuceyeski, A.: Neurogen: activation optimized image synthesis for discovery neuroscience. NeuroImage 247
2022
Cited alongside, same era.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16000–16009 (2022)
2022
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
2022
Cited alongside, same era.
Lin, S., Sprague, T., Singh, A.K.: Mind reader: Reconstructing complex images from brain activities. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
2023
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Ozcelik, F., VanRullen, R.: Natural scene reconstruction from fmri signals using generative latent diffusion. Scientific Reports 13
2023
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2023
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2023
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Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22500–22510 (2023)
2023
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2023
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Takagi, Y., Nishimoto, S.: High-resolution image reconstruction with latent diffusion models from human brain activity. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14453–14463 (2023)
2023
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Xu, X., Wang, Z., Zhang, G., Wang, K., Shi, H.: Versatile diffusion: Text, images and variations all in one diffusion model. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7754–7765 (2023)
2023
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2023
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Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)
2023
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Fang, T., Zheng, Q., Pan, G.: Alleviating the semantic gap for generalized fmri-to-image reconstruction. Advances in Neural Information Processing Systems 36
2024
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