Saliency Detection Using Adversarial Learning Networks

Abstract This paper proposes a novel model for saliency detection using the adversarial learning networks, in which the generator is used to generate the saliency map and the discriminator is deployed to guide the training process of overall network. Concretely, the training procedure of our model consists of three steps including the training of generator, the training of discriminator, and the training throughout the overall network. The key point of training process lies in the discriminator, which is designed to provide the feedback information for the acceleration of the generator and the refinement of saliency map. Therefore, during the training stage of overall network, the output of the generator, i.e. the coarse saliency map, is fed into the discriminator, yielding the corresponding feedback information. Following this way, we can obtain the final generator with a higher performance. For testing, the obtained generator is employed to perform saliency detection. Extensive experiments on four challenging saliency detection datasets show that our model not only achieves the favorable performance against the state-of-the-art saliency models, but also possesses the faster convergence speed when training the proposed model.