RPA: Residual Patch-Token Adapter for Image Retrieval from EEG and MEG

arXiv:2609.31698 · cs.CV · Submitted 2026-09-19 · Read on arXiv

cs.CV

Submitted: 2026-09-19

Updated: 2026-10-06

Terminology

• Accuracy — 58× in this paper · explained in 1 episode
• Alignment — 44× in this paper · explained in 1 episode
• Embeddings — 29× in this paper · explained in 2 episodes
• Cosine Similarity — 12× in this paper · explained in 3 episodes
• Foundation Model — 11× in this paper · explained in 3 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Residual Patch Adapter — 6× in this paper
• Residualization — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Linear probe — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptive Teaching — 1× in this paper · explained in 1 episode
• Granularity Mismatch — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes

Sources

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Aligning What EEG Can See: Structural Representations for Brain-Vision Matching
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

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