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Found 5 papers, 4 papers with code
Brain Captioning: Decoding human brain activity into images and text
Our brain captioning approach outperforms existing methods, while our image reconstruction pipeline generates plausible images with improved spatial relationships.
Natural scene reconstruction from fMRI signals using generative latent diffusion
In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images.
Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs
Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research.
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks
Lastly, we show the contribution of the self-supervision tasks to the GAN training on the loss landscape and present that the effects of these tasks may not be cooperative to the adversarial training in some settings.
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs
Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years.
