Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements

by   Leonard E. van Dyck, et al.

Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, and consequently indicate strong architectural and functional parallelism with the ventral visual pathway throughout comparisons with neuroimaging and neural time series data. As recent advances in deep learning seem to decrease this similarity, computational neuroscience is challenged to reverse-engineer the biological plausibility to obtain useful models. While previous studies have shown that biologically inspired architectures are able to amplify the human-likeness of the models, in this study, we investigate a purely data-driven approach. We use human eye tracking data to directly modify training examples and thereby guide the models' visual attention during object recognition in natural images either towards or away from the focus of human fixations. We compare and validate different manipulation types (i.e., standard, human-like, and non-human-like attention) through GradCAM saliency maps against human participant eye tracking data. Our results demonstrate that the proposed guided focus manipulation works as intended in the negative direction and non-human-like models focus on significantly dissimilar image parts compared to humans. The observed effects were highly category-specific, enhanced by animacy and face presence, developed only after feedforward processing was completed, and indicated a strong influence on face detection. With this approach, however, no significantly increased human-likeness was found. Possible applications of overt visual attention in DCNNs and further implications for theories of face detection are discussed.


page 5

page 12

page 25

page 26


Comparing object recognition in humans and deep convolutional neural networks – An eye tracking study

Deep convolutional neural networks (DCNNs) and the ventral visual pathwa...

Modeling Biological Face Recognition with Deep Convolutional Neural Networks

Deep Convolutional Neural Networks (DCNNs) have become the state-of-the-...

Behind the Machine's Gaze: Biologically Constrained Neural Networks Exhibit Human-like Visual Attention

By and large, existing computational models of visual attention tacitly ...

Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning

It is a long-term goal to transfer biological processing principles as w...

Saliency Driven Object recognition in egocentric videos with deep CNN

The problem of object recognition in natural scenes has been recently su...

Please sign up or login with your details

Forgot password? Click here to reset