5 papers
MAPS: Masked Attribution-based Probing of Strategies- A computational framework to align human and model explanations
Sabine Muzellec, Yousif Kashef Alghetaa, Simon Kornblith +1
Human core object recognition depends on the selective use of visual information, but the strategies guiding these choices are difficult to measure directly. We present MAPS (Maske…
Aligning Machine and Human Visual Representations across Abstraction Levels
Lukas Muttenthaler, Klaus Greff, Frieda Born +6
Deep neural networks have achieved success across a wide range of applications, including as models of human behavior and neural representations in vision tasks. However, neural ne…
Objective drives the consistency of representational similarity across datasets
Laure Ciernik, Lorenz Linhardt, Marco Morik +3
The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irre…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…
When Does Perceptual Alignment Benefit Vision Representations?
Shobhita Sundaram, Stephanie Fu, Lukas Muttenthaler +5
Humans judge perceptual similarity according to diverse visual attributes, including scene layout, subject location, and camera pose. Existing vision models understand a wide range…