# Capacity limitations of visual search in deep convolutional neural networks

arXiv: https://arxiv.org/abs/1707.09775 · HTML: https://mlsanity.com/paper/1707.09775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [CNN-based search model underestimates attention guidance by simple visual features](https://mlsanity.com/paper/2103.15439/) (2021)

## Abstract (arXiv)

Deep convolutional neural networks follow roughly the architecture of biological visual systems and have shown a performance comparable to human observers in object recognition tasks. In this study, I tested three pretrained deep neural networks in visual search for simple visual features, and for feature configurations. The results reveal a qualitative difference from human performance. It appears that there is no clear difference between searches for simple features that pop out in experiments with humans, and for feature configurations that exhibit strict capacity limitations in human vision. Both types of stimuli reveal comparable capacity limitations in the neural networks tested here.