Gated spatio and temporal convolutional neural network for activity recognition: towards gated multimodal deep learning

Abstract

Human activity recognition requires both visual and temporal cues, making it challenging to integrate these important modalities. The usual schemes for integration are averaging and fixing the weights of both features for all samples. However, how much weight is needed for each sample and modality, is still an open question. A mixture of experts via a gating Convolutional Neural Network (CNN) is one promising architecture for adaptively weighting every sample within a dataset. In this paper, rather than just averaging or using fixed weights, we investigate how a natural associative cortex such as a network integrates expert networks to form a gating CNN scheme. Starting from Red Green Blue color model (RGB) values and optical flows, we show that with proper treatment, the gating CNN scheme works well, indicating future approaches to information integration in future activity recognition.