Saliency Detection With Fully Convolutional Neural Network
This work addresses saliency detection for image processing applications, but it appears incremental as it builds on existing VGG-16 architecture.
The paper tackles saliency detection in images by proposing a fully convolutional neural network that uses part of VGG-16, resulting in an approach that leverages pretrained weights for enhanced accuracy.
Saliency detection is an important task in image processing as it can solve many problems and it usually is the first step in for other processes. Convolutional neural networks have been proved to be very effective on several image processing tasks such as classification, segmentation, semantic colorization and object manipulation. Besides, using the weights of a pretrained networks is a common practice for enhancing the accuracy of a network. In this paper a fully convolutional neural network which uses a part of VGG-16 is proposed for saliency detection in images.