ArticlePLoS computational biology2020
Capsule networks as recurrent models of grouping and segmentation.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Are we ready to tackle perceptual segmentation of natural scenes?Vision research · 2026Review
- Temporal windows of perceptual organization: Evidence from crowding and uncrowding.Journal of vision · 2025Article
- Dynamics of vision: Grouping takes longer than crowding.Journal of vision · 2025Article
- A feedforward mechanism for human-like contour integration.PLoS computational biology · 2025Article
- The visibility of Eidolon distortions in things and stuff.Journal of vision · 2025Article
- Neuroscientific insights about computer vision models: a concise review.Biological cybernetics · 2024Review
- The attentive reconstruction of objects facilitates robust object recognition.PLoS computational biology · 2024Article
- Using unsupervised capsule neural network reveal spatial representations in the human brain.Human brain mapping · 2024Article
- Measuring uncertainty in human visual segmentation.PLoS computational biology · 2023Article
- Generalizing biological surround suppression based on center surround similarity via deep neural network models.PLoS computational biology · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- A brain-inspired object-based attention network for multiobject recognition and visual reasoning.Journal of vision · 2023Article
- Intact and deficient contextual processing in schizophrenia patients.Schizophrenia research. Cognition · 2022Article
- The Irreducibility of Vision: Gestalt, Crowding and the Fundamentals of Vision.Vision (Basel, Switzerland) · 2022Review
- Mixture-modeling approach reveals global and local processes in visual crowding.Scientific reports · 2022Article
- From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction.Journal of vision · 2022Article
- Configural relations in humans and deep convolutional neural networks.Frontiers in artificial intelligence · 2022Article
- Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing.Journal of vision · 2021Article
- Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity.Patterns (New York, N.Y.) · 2021Article
- Emergence of crowding: The role of contrast and orientation salience.Journal of vision · 2021Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we previously showed that no classic model of vision, including ffCNNs, can explain human global shape processing. Here, we show that Capsule Neural Networks (CapsNets), combining ffCNNs with recurrent grouping and segmentation, solve this challenge. We also show that ffCNNs and standard recurrent CNNs do not, suggesting that the grouping and segmentation capabilities of CapsNets are crucial. Furthermore, we provide psychophysical evidence that grouping and segmentation are implemented recurrently in humans, and show that CapsNets reproduce these results well. We discuss why recurrence seems needed to implement grouping and segmentation efficiently. Together, we provide mutually reinforcing psychophysical and computational evidence that a recurrent grouping and segmentation process is essential to understand the visual system and create better models that harness global shape computations.
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