This paper proposes a novel saliency prediction model for children with autism spectrum disorder (ASD). Based on the convolutional neural network, the multi-level features are extracted and integrated to three attention maps, which are used to generate the predicted saliency map. The deep supervision on the attention maps is exploited to build connections between ground truths and the deep layers in the neural network during training. Furthermore, by performing the single-side clipping operation on the ground truths, our model is encouraged to enhance the capacity of better predicting the most salient regions in images. Experimental results on an ASD eye-tracking dataset demonstrate that our model achieves the better saliency prediction performance for children with ASD.

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This paper presents a novel saliency prediction model for children with autism spectrum disorder (ASD). We design a new convolution neural network and train it with a new ASD dataset. Among the contributions, we can cite the coarse-to-fine architecture as well as the loss function which embeds a regularization term. We also discuss about some data augmentation methods for ASD dataset. Experimental results show that the proposed model performs better than 6 models, one supervised model finetuned with the ASD dataset. Contrary to control people, our results hint that no center bias apply in visuall attention for autistic children.