Convolutional Recurrent Neural Networks for Better Image Understanding
Alexis Vallet, Hiroyasu Sakamoto · 2016
Although deep convolutional neural networks have brought basic computer vision tasks to unprecedented accuracy, the best models still struggle to produce higher level image understanding. Indeed, current models for tasks such as visual question answering, often based on recurrent neural networks, have difficulties surpassing baseline methods. We suspect that this is due in part to spatial information in the image not being properly leveraged. We attempt to solve these difficulties by introducing a recurrent unit able to keep and process spatial information throughout the network. On a simple task, we show that our method is significantly more accurate than alternative baselines which discard spatial information. We also demonstrate that higher resolution input performs better than lower resolution input to a surprising degree, even when the input features are less discriminative. Notably, we show that our approach based on higher resolution input is better able to detect details of the images such as the precise number of objects, and the presence of smaller objects, while being less sensitive to biases in the label distribution of the training set.