Zero-shot counting with a dual-stream neural network model
arxiv(2024)
摘要
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.
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