Spatial Attentional Bilinear 3D Convolutional Network for Video-Based Autism Spectrum Disorder Detection

Video-based Autism Spectrum Disorder (ASD) detection is a challenge to most video classification networks due to the high degree of similarity between categories. Bilinear pooling is a second-order method, which is widely used in fine-grained visual recognition. However, the average summation in bilinear pooling limits its ability to perceive spatial information, which is detrimental to fine-grained visual recognition. In this paper, we propose spatial attentional bilinear pooling to enhance its spatial information extraction without significantly increasing the parameters. Further, we propose a fine-grained action recognition network named SA-B3D with LSTM model for video-based ASD detection. The proposed model can focus on more discriminative regions dynamically and effectively. Compared with state-of-the-art models, the proposed model achieves significant improvement on video-based ASD dataset.