CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework

Abstract

In this paper, we propose a framework for evaluation of co-salient object detection algorithms along with two novel CoSOD methods. The processing pipeline of this framework is based on the CoEGNet algorithm, where the saliency detection part can be easily replaced by any saliency detector to be evaluated. By leveraging the proposed framework, we developed two new algorithms: one based on U2Net and the other based on the label decoupling framework (LDF). They are called in this paper CoU2Net and CoLDF, respectively. The proposed solutions were tested on three datasets: CoCA, CoSal2015, and CoSOD3k, and compared with some of the best algorithms in co-silent object detection: GICD and CoEGNet. The advantages and disadvantages of the proposed methods are highlighted and discussed. As a generalization of the aforementioned methods, a framework called Sal.Co has been also proposed. It is a modification of the CoEGNet method and it works on a saliency mask obtained from a saliency detector and attempts to indicate salient objects coexisting in a group of images. Both CoLDF and CoU2Net achieved better results than CoEGNet on the CoCA dataset. On the CoSal2015 and CoSOD3k datasets, they performed similarly to state-of-the-art methods, while maintaining a highly customizable structure. The source code can be found at: https://github.com/jakubkorczakowski/cosal_sal_testing. INDEX TERMS deep learning, image analysis, object detection, pattern recognition I. INTRODUCTION