Brain-Inspired Attention Model for Object Counting

Abstract

We develop a sequential Q-learning model using a recurrent neural network to count objects in images using attentional search. The proposed model, which is based on visual attention, scans images by making a sequence of attentional jumps or saccades. By integrating the information gathered by the sequence of saccades, the model counts the number of targets in the image. The model consists primarily of two modules: the Classification Network and the Saccade Network. Whereas the Classification network predicts the number of target objects in the image, the Saccade network predicts the next saccadic jump. When the probability of the best predicted class crosses a threshold, the model halts making saccades and outputs its class prediction. Correct prediction results in positive reward, which is used to train the model by Q-learning. We achieve an accuracy of 92.1% in object counting. Simulations show that there is a direct relation between the number of glimpses required and the number of objects present to achieve a high accuracy in object counting.