TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals

Alexander Vedernikov, Puneet Kumar, Haoyu Chen, Tapio Seppänen, Xiaobai Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4723-4732

Engagement analysis finds various applications in healthcare education advertisement services. Deep Neural Networks used for analysis possess complex architecture and need large amounts of input data computational power inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset the proposed method outperforms existing baselines utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore comparative analysis shows TCCT-Net's architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https://github.com/vedernikovphoto/TCCT_Net.