Zero-shot counting with a dual-stream neural network model
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913352660287488 |
|---|---|
| author | Thompson, Jessica A. F. Sheahan, Hannah Dumbalska, Tsvetomira Sandbrink, Julian Piazza, Manuela Summerfield, Christopher |
| author_facet | Thompson, Jessica A. F. Sheahan, Hannah Dumbalska, Tsvetomira Sandbrink, Julian Piazza, Manuela Summerfield, Christopher |
| contents | Deep neural networks have provided a computational framework for understanding object recognition, grounded in the neurophysiology of the primate ventral stream, but fail to account for how we process relational aspects of a scene. For example, deep neural networks fail at problems that involve enumerating the number of elements in an array, a problem that in humans relies on parietal cortex. Here, we build a 'dual-stream' neural network model which, equipped with both dorsal and ventral streams, can generalise its counting ability to wholly novel items ('zero-shot' counting). In doing so, it forms spatial response fields and lognormal number codes that resemble those observed in macaque posterior parietal cortex. We use the dual-stream network to make successful predictions about behavioural studies of the human gaze during similar counting tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09953 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Zero-shot counting with a dual-stream neural network model Thompson, Jessica A. F. Sheahan, Hannah Dumbalska, Tsvetomira Sandbrink, Julian Piazza, Manuela Summerfield, Christopher Neurons and Cognition Deep neural networks have provided a computational framework for understanding object recognition, grounded in the neurophysiology of the primate ventral stream, but fail to account for how we process relational aspects of a scene. For example, deep neural networks fail at problems that involve enumerating the number of elements in an array, a problem that in humans relies on parietal cortex. Here, we build a 'dual-stream' neural network model which, equipped with both dorsal and ventral streams, can generalise its counting ability to wholly novel items ('zero-shot' counting). In doing so, it forms spatial response fields and lognormal number codes that resemble those observed in macaque posterior parietal cortex. We use the dual-stream network to make successful predictions about behavioural studies of the human gaze during similar counting tasks. |
| title | Zero-shot counting with a dual-stream neural network model |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2405.09953 |