arXiv Computer Vision
Align, Then Adapt: Few-Shot fMRI Visual Reconstruction For New Subjects
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arXiv AI
2d ago
Latent Prediction Yields Cross-Subject Alignable Representations in Sensory-Evoked fMRI
The study investigates whether self‑supervised learning can produce useful representations from fMRI data alone. Using a joint embedding predictive architecture, participant‑specific encoders are trained without access to stimulus images or derived representations. The resulting embeddings perform competitively on brain captioning, image reconstruction, and cross‑participant generalization, and can be aligned unsupervised across participants, enabling a decoding head trained on one subject to work on another with minimal loss of performance.
arXiv AI
Jun 16
Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE
arXiv:2606. 15989v1 Announce Type: cross Abstract: Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders.
arXiv AI
Jun 4
Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
arXiv:2606. 04772v1 Announce Type: cross Abstract: Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience.
Hugging Face Trending Papers
Jun 3
Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.
arXiv Computer Vision
Oct 19
Alignment of visual brain encoding models with individual perceptual similarity judgments
The study explores how to fine‑tune visual brain encoding models so that their internal representations better match individual human perceptual similarity judgments. Using similarity data from 100 images collected from eight participants in the Natural Scenes Dataset, the authors adapted a generic similarity model (DreamSim) to create personalized perceptual similarity models for each subject. They then fine‑tuned each participant’s baseline encoder with either generic or personalized similarity information, finding that models trained with personalized judgments were most aligned with human perceptual similarity and preserved inter‑subject correlations of those judgments.
arXiv Computer Vision
Aug 24
Deep Models, Shallow Alignment: Uncovering the Granularity Mismatch in Neural Decoding
arXiv:2601.21948v2 Announce Type: replace Abstract: Neural visual decoding is a central problem in brain-computer interface research, aiming to reconstruct human visual perception and to elucidate th...