Cross-attention encoding models reveal dynamic spatiotemporal routing across human higher visual cortex

arXiv:2609.36366 · q-bio.NC, cs.AI, cs.LG · Submitted 2026-09-28 · Read on arXiv

q-bio.NC, cs.AI, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Accuracy — 12× in this paper · explained in 1 episode
• Alignment — 6× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Clustering — 4× in this paper · explained in 2 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Joint Attention — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Conference on Cognitive Computational Neuroscience — 1× in this paper
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Transformer brain encoders explain human high-level visual responses
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Perception Encoder: The best visual embeddings are not at the output of the network
• SAM 3: Segment Anything with Concepts
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies
• Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity
• DINOv3
• NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity

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