Journal
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Volume -, Issue -, Pages 1601-1609Publisher
IEEE
DOI: 10.1109/CVPR.2016.177
Keywords
-
Categories
Ask authors/readers for more resources
Recent advances in neural networks have revolutionized computer vision, but these algorithms are still outperformed by humans. Could this performance gap be due to systematic differences between object representations in humans and machines? To answer this question we collected a large dataset of 26,675 perceived dissimilarity measurements from 2,801 visual objects across 269 human subjects, and used this dataset to train and test leading computational models. The best model (a combination of all models) accounted for 68% of the explainable variance. Importantly, all computational models showed systematic deviations from perception: (1) They underestimated perceptual distances between objects with symmetry or large area differences; (2) They overestimated perceptual distances between objects with shared features. Our results reveal critical elements missing in computer vision algorithms and point to explicit encoding of these properties in higher visual areas in the brain.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available