Brain-IT-VQA decodes visual question answers from fMRI using a transformer to extract language tokens and introduces the NSD-VQA benchmark with 20 controlled questions per image across 20 categories.
UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activity, August 2023
7 Pith papers cite this work. Polarity classification is still indexing.
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NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
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.
BrainJanus presents a unified autoregressive model with a brain tokenizer that maps between neural activity, vision, and language for encoding and decoding tasks.
MindAdapter introduces a decoupled linear-residual adapter with topology-anchored dual-stream manifold constraints for few-shot cross-subject calibration of brain-to-visual models, improving reconstruction and retrieval on NSD.
StableMind achieves source-free cross-subject fMRI decoding via ridge-projection priors, Fourier brain augmentation, and difficulty-aware image blur, reaching 84.02% image and 81.66% brain retrieval accuracy on the Natural Scenes Dataset with a 5.71% gain over SOTA under 1-hour adaptation.
citing papers explorer
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Brain-IT-VQA: From Brain Signals to Answers
Brain-IT-VQA decodes visual question answers from fMRI using a transformer to extract language tokens and introduces the NSD-VQA benchmark with 20 controlled questions per image across 20 categories.
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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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BrainJanus: A Unified Model for Understanding and Generation across Brain, Vision, and Language
BrainJanus presents a unified autoregressive model with a brain tokenizer that maps between neural activity, vision, and language for encoding and decoding tasks.
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MindAdapter: Few-Shot Parameter-Efficient Residual Calibration of Cross-Subject Brain-to-Visual Decoding Models
MindAdapter introduces a decoupled linear-residual adapter with topology-anchored dual-stream manifold constraints for few-shot cross-subject calibration of brain-to-visual models, improving reconstruction and retrieval on NSD.
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StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation
StableMind achieves source-free cross-subject fMRI decoding via ridge-projection priors, Fourier brain augmentation, and difficulty-aware image blur, reaching 84.02% image and 81.66% brain retrieval accuracy on the Natural Scenes Dataset with a 5.71% gain over SOTA under 1-hour adaptation.