1. A method of processing data within a convolutional attention recurrent neural network (RNN), comprising: generating a current multi-dimensional attention map, the current multi-dimensional attention map indicating areas of interest in a first frame from a sequence of spatio-temporal data; receiving a multi-dimensional feature map; convolving the current multi-dimensional attention map and the multi-dimensional feature map to obtain a multi-dimensional hidden state and a next multi-dimensional attention map; and identifying a class of interest in the first frame based on the multi-dimensional hidden state and training data.