Assessing The Importance Of Colours For CNNs In Object Recognition
- URL: http://arxiv.org/abs/2012.06917v1
- Date: Sat, 12 Dec 2020 22:55:06 GMT
- Title: Assessing The Importance Of Colours For CNNs In Object Recognition
- Authors: Aditya Singh, Alessandro Bay and Andrea Mirabile
- Abstract summary: Convolutional neural networks (CNNs) have been shown to exhibit conflicting properties.
We demonstrate that CNNs often rely heavily on colour information while making a prediction.
We evaluate a model trained with congruent images on congruent, greyscale, and incongruent images.
- Score: 70.70151719764021
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Humans rely heavily on shapes as a primary cue for object recognition. As
secondary cues, colours and textures are also beneficial in this regard.
Convolutional neural networks (CNNs), an imitation of biological neural
networks, have been shown to exhibit conflicting properties. Some studies
indicate that CNNs are biased towards textures whereas, another set of studies
suggests shape bias for a classification task. However, they do not discuss the
role of colours, implying its possible humble role in the task of object
recognition. In this paper, we empirically investigate the importance of
colours in object recognition for CNNs. We are able to demonstrate that CNNs
often rely heavily on colour information while making a prediction. Our results
show that the degree of dependency on colours tend to vary from one dataset to
another. Moreover, networks tend to rely more on colours if trained from
scratch. Pre-training can allow the model to be less colour dependent. To
facilitate these findings, we follow the framework often deployed in
understanding role of colours in object recognition for humans. We evaluate a
model trained with congruent images (images in original colours eg. red
strawberries) on congruent, greyscale, and incongruent images (images in
unnatural colours eg. blue strawberries). We measure and analyse network's
predictive performance (top-1 accuracy) under these different stylisations. We
utilise standard datasets of supervised image classification and fine-grained
image classification in our experiments.
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