NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
Brain captioning: Decoding human brain activity into images and text
4 Pith papers cite this work. Polarity classification is still indexing.
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MIRAGE achieves state-of-the-art mental image reconstruction from fMRI on the NSD-Imagery benchmark by using a linear backbone with multi-modal text and image features fed to a diffusion model.
A meta-optimized in-context learning approach enables training-free cross-subject semantic visual decoding from fMRI by inferring individual neural encoding patterns via hierarchical inference on a few examples.
FPED is a functional-network prior-guided MoE framework for fMRI visual reconstruction that claims competitive performance at 0.68B parameters and biologically meaningful routing interpretability.
citing papers explorer
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NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity
NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
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MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery
MIRAGE achieves state-of-the-art mental image reconstruction from fMRI on the NSD-Imagery benchmark by using a linear backbone with multi-modal text and image features fed to a diffusion model.
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Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
A meta-optimized in-context learning approach enables training-free cross-subject semantic visual decoding from fMRI by inferring individual neural encoding patterns via hierarchical inference on a few examples.
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FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding
FPED is a functional-network prior-guided MoE framework for fMRI visual reconstruction that claims competitive performance at 0.68B parameters and biologically meaningful routing interpretability.