# Zero-shot counting with a dual-stream neural network model

arXiv: https://arxiv.org/abs/2405.09953 · HTML: https://mlsanity.com/paper/2405.09953/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2024.

## Built on by (most cited first, 4 of 4)

- [Sequential Enumeration in Large Language Models](https://mlsanity.com/paper/2512.04727/) (2025)
- [Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex](https://mlsanity.com/paper/2511.12715/) (2025)
- [NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity](https://mlsanity.com/paper/2607.02317/) (2026)
- [Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot](https://mlsanity.com/paper/2604.11373/) (2026)

## Abstract (arXiv)

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.