An hardware-aware image polarity detector enhanced with visual attention

Edoardo Ragusa, Tommaso Apicella, Christian Gianoglio, Rodolfo Zunino, Paolo Gastaldo

Abstract: Providing user customized experience is one of the main goals for present-day electronic smart devices. Image polarity detection plays a crucial role in understanding users' preferences due to the fact that information is massively represented by means of pictures. State-of-the-art frameworks are based on deep learning networks and continue evolving adding sophisticated structures to enhance generalization performances of the inference systems. Recent works proved that image analysis can be enhanced exploiting the information about salient regions. However, better performances are obtained at the cost of a higher computational load. This paper presents a hardware-friendly deep learning framework for image polarity detectors based on salient regions of an image. Experimental results show the reliable performances of the proposed solution on real-world data.