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.

By Daniel Chong, Haomiao Chen, Keith W Jamison, Raja Marjieh, Ilia Sucholutsky, Nori Jacoby, Amy Kuceyeski
arXiv AI
Oct 2

Modeling The Object Representations Underlying Human Physical Reasoning

The study investigates how vision models represent objects compared to humans during physical reasoning tasks. By comparing segmentation outputs from several architectures (DINOv2, SegFormer, DeepLabV3+, UPerNet) to human behavior in time‑to‑collision and change‑detection tasks, the authors find that briefly trained models are too coarse, fully trained models are too fine, and an intermediate training stage best matches the coarse, volumetric bodies humans use. Larger models reach this intermediate alignment earlier, suggesting that resource constraints drive the emergence of human‑like representations in general‑purpose vision systems.

By Andrey Gizdov, Andrea Procopio, Lorenzo Caputi, Georgi I. Ivanov, Yichen Li, Daniel Harari, Tomer Ullman