# Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation

arXiv: https://arxiv.org/abs/2007.06294 · HTML: https://mlsanity.com/paper/2007.06294/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study](https://mlsanity.com/paper/2108.00107/) (2021)
- [Brain informed transfer learning for categorizing construction hazards](https://mlsanity.com/paper/2211.12420/) (2022)
- [Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations](https://mlsanity.com/paper/2603.08317/) (2026)

## Abstract (arXiv)

For a considerable time, deep convolutional neural networks (DCNNs) have reached human benchmark performance in object recognition. On that account, computational neuroscience and the field of machine learning have started to attribute numerous similarities and differences to artificial and biological vision. This study aims towards a behavioral comparison of visual core object recognition performance between humans and feedforward neural networks in a classification learning paradigm on an ImageNet data set. For this purpose, human participants (n = 65) competed in an online experiment against different feedforward DCNNs. The designed approach based on a typical learning process of seven different monkey categories included a training and validation phase with natural examples, as well as a testing phase with novel, unexperienced shape and color manipulations. Analyses of accuracy revealed that humans not only outperform DCNNs on all conditions, but also display significantly greater robustness towards shape and most notably color alterations. Furthermore, a precise examination of behavioral patterns highlights these findings by revealing independent classification errors between the groups. The obtained results show that humans contrast strongly with artificial feedforward architectures when it comes to visual core object recognition of manipulated images. In general, these findings are in line with a growing body of literature, that hints towards recurrence as a crucial factor for adequate generalization abilities.