Visual Attention Models of Object Counting
We develop a sequential learning model using a recurrent neural network architecture and reinforcement learning to recognize and count objects in images. Simple feedforward neural networks perform well on this task when trained using backpropagation; however, convolutional neural networks are computationally expensive and results are less certain when the image input has imperfect resolution outside of the focus area (like input to the human visual system) We build an architecture based on visual attention models implemented in the literature, such as by Mnih et. al. applied to the task of counting objects in images. Our model scans an image for distinguishing features based on models of the retina, layering blurriness on the image to simulate focus on a particular area of the image. Our recurrent neural network sequentially incorporates the data from inputs to produce an accurate count of the objects in the entire image, and reinforcement learning dictates glimpse locations for each successive iterative step. We achieve accuracy of 66.7 percent, and we prove that the glimpse model tracks discrete object figures in images through the relationship between the number of glimpses required to yield accurate estimates and the number of objects present.