# CNN-based search model underestimates attention guidance by simple visual features

arXiv: https://arxiv.org/abs/2103.15439 · HTML: https://mlsanity.com/paper/2103.15439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Finding any Waldo: zero-shot invariant and efficient visual search](https://mlsanity.com/paper/1807.10587/) (2018)
- [Capacity limitations of visual search in deep convolutional neural networks](https://mlsanity.com/paper/1707.09775/) (2017)

## Abstract (arXiv)

Recently, Zhang et al. (2018) proposed an interesting model of attention guidance that uses visual features learnt by convolutional neural networks for object recognition. I adapted this model for search experiments with accuracy as the measure of performance. Simulation of our previously published feature and conjunction search experiments revealed that CNN-based search model considerably underestimates human attention guidance by simple visual features. A simple explanation is that the model has no bottom-up guidance of attention. Another view might be that standard CNNs do not learn features required for human-like attention guidance.