Abstract
Previous saliency detection methods usually focused on extracting powerful discriminative features to describe images with a complex background. Recently, the generative adversarial network (GAN) has shown a great ability in feature learning for synthesizing high quality natural images. Since the GAN shows a superior feature learning ability, we present a new multi-scale adversarial feature learning (MAFL) model for image saliency detection. In particular, we build this model, which is composed of two convolutional neural network (CNN) modules: the multi-scale G-network takes natural images as inputs and generates the corresponding synthetic saliency map, and we design a novel layer in the D-network, namely a correlation layer, which is used to determine whether one image is a synthetic saliency map or ground-truth saliency map. Quantitative and qualitative comparisons on several public datasets show the superiority of our approach.
Highlights
Saliency was originally defined in the field of neuroscience and psychology and later introduced into biology to stimulate the human visual attention mechanism
Different from previous work on saliency detection, we propose a new model named “multi-scale adversarial feature learning” (MAFL), which is built upon DCGAN
Modules: the multi-scale G-network takes natural images as inputs and generates the corresponding synthetic saliency map, and we design a novel layer in the D-network, namely a correlation layer, which is used to determine whether one image is a synthetic saliency map or ground-truth saliency map
Summary
Saliency was originally defined in the field of neuroscience and psychology and later introduced into biology to stimulate the human visual attention mechanism. This term denotes that humans have the ability to find salient regions (regions of interest) in the image. Visual saliency has been incorporated into various computer vision tasks, such as image caption [1], video summarization [2], image retrieval [3], person re-identification [4], object detection [5], etc. Saliency detection has become a well-known research topic. Due to the rapid development of artificial intelligence, it enables computers to automatically learn features to find a salient object
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
