113 citations · 150 across the 6 of their papers we have counts for
6 papers
Through their eyes: multi-subject Brain Decoding with simple alignment techniques
Matteo Ferrante, Tommaso Boccato, Nicola Toschi
Previous brain decoding research primarily involves single-subject studies, reconstructing stimuli via fMRI activity from the same subject. Our study aims to introduce a generaliza…
Brain Captioning: Decoding human brain activity into images and text
Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato +2
Every day, the human brain processes an immense volume of visual information, relying on intricate neural mechanisms to perceive and interpret these stimuli. Recent breakthroughs i…
Natural scene reconstruction from fMRI signals using generative latent diffusion
Furkan Ozcelik, Rufin VanRullen
In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creati…
Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs
Furkan Ozcelik, Bhavin Choksi, Milad Mozafari +2
Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research. Prior studies had success in reconstructing either the low…
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks
Gulcin Baykal, Furkan Ozcelik, Gozde Unal
Generative Adversarial Networks (GANs) have become the most used networks towards solving the problem of image generation. Self-supervised GANs are later proposed to avoid the cata…
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs
Furkan Ozcelik, Ugur Alganci, Elif Sertel +1
Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years. However, they still exhibit limitations in p…