ArticleFrontiers in computational neuroscience2016
Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.
Article in Frontiers in computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Recurrent issues with deep neural network models of visual recognition.Scientific reports · 2025Article
- Distinguishing mirror from glass: A "big data" approach to material perception.Journal of vision · 2022Article
- Oculo-retinal dynamics can explain the perception of minimal recognizable configurations.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Cognitive Relevance Transform for Population Re-Targeting.Sensors (Basel, Switzerland) · 2020Article
- The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Beyond core object recognition: Recurrent processes account for object recognition under occlusion.PLoS computational biology · 2019Article
- The roles of supervised machine learning in systems neuroscience.Progress in neurobiology · 2019Review
- A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning.NeuroImage. Clinical · 2018Article
- Perceptual Dominance in Brief Presentations of Mixed Images: Human Perception vs. Deep Neural Networks.Frontiers in computational neuroscience · 2018Article
- Perception Science in the Age of Deep Neural Networks.Frontiers in psychology · 2017Article
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4 authors.
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Abstract
View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call "variation level." We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.
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