Weakly Supervised Learning for Salient Object Detection

TL;DR

This paper introduces a weakly supervised learning method for salient object detection that jointly addresses object existence and detection, reducing the need for expensive pixel-wise annotations and improving performance on background images.

Contribution

It proposes a novel integrated framework using latent SVM to handle both salient object existence and detection with weak supervision.

Findings

Effective on benchmark datasets

Handles background images without salient objects

Reduces annotation costs

Abstract

Recent advances in supervised salient object detection has resulted in significant performance on benchmark datasets. Training such models, however, requires expensive pixel-wise annotations of salient objects. Moreover, many existing salient object detection models assume that at least one salient object exists in the input image. Such an assumption often leads to less appealing saliency maps on the background images, which contain no salient object at all. To avoid the requirement of expensive pixel-wise salient region annotations, in this paper, we study weakly supervised learning approaches for salient object detection. Given a set of background images and salient object images, we propose a solution toward jointly addressing the salient object existence and detection tasks. We adopt the latent SVM framework and formulate the two problems together in a single integrated objective function: saliency labels of superpixels are modeled as hidden variables and involved in a classification term conditioned to the salient object existence variable, which in turn depends on both global image and regional saliency features and saliency label assignment. Experimental results on benchmark datasets validate the effectiveness of our proposed approach.