ArticleJournal of vision2023
A brain-inspired object-based attention network for multiobject recognition and visual reasoning.
Article in Journal of vision, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Are we ready to tackle perceptual segmentation of natural scenes?Vision research · 2026Review
- Top-down and bottom-up attention for joint pattern classification and reconstruction.PloS one · 2026Article
- The attentive reconstruction of objects facilitates robust object recognition.PLoS computational biology · 2024Article
- A Deep Recurrent Learning-Based Region-Focused Feature Detection for Enhanced Target Detection in Multi-Object Media.Sensors (Basel, Switzerland) · 2023Article
- Self-supervised transformers predict dynamics of object-based attention in humans.Proceedings of the ... Conference on Cognitive Computational Neuroscience · 2023Article
- Using Generated Object Reconstructions to Study Object-based Attention.Proceedings of the ... Conference on Cognitive Computational Neuroscience · 2023Article
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Authors and funding
3 authors.
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Abstract
The visual system uses sequences of selective glimpses to objects to support goal-directed behavior, but how is this attention control learned? Here we present an encoder-decoder model inspired by the interacting bottom-up and top-down visual pathways making up the recognition-attention system in the brain. At every iteration, a new glimpse is taken from the image and is processed through the "what" encoder, a hierarchy of feedforward, recurrent, and capsule layers, to obtain an object-centric (object-file) representation. This representation feeds to the "where" decoder, where the evolving recurrent representation provides top-down attentional modulation to plan subsequent glimpses and impact routing in the encoder. We demonstrate how the attention mechanism significantly improves the accuracy of classifying highly overlapping digits. In a visual reasoning task requiring comparison of two objects, our model achieves near-perfect accuracy and significantly outperforms larger models in generalizing to unseen stimuli. Our work demonstrates the benefits of object-based attention mechanisms taking sequential glimpses of objects.
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