Synthesize Boundaries: A Boundary-aware Self-consistent Framework for Weakly Supervised Salient Object Detection

TL;DR

This paper introduces a boundary-aware self-consistent framework for weakly supervised salient object detection that leverages synthetic images to learn precise object boundaries without extra data, outperforming existing methods.

Contribution

The paper proposes a novel self-consistent framework with synthetic boundary creation and dual-branch training to improve boundary accuracy in weakly supervised SOD.

Findings

Outperforms state-of-the-art weakly supervised SOD methods on five benchmarks.

Effectively learns precise boundaries without additional auxiliary data.

Reduces the gap between weakly and fully supervised SOD methods.

Abstract

Fully supervised salient object detection (SOD) has made considerable progress based on expensive and time-consuming data with pixel-wise annotations. Recently, to relieve the labeling burden while maintaining performance, some scribble-based SOD methods have been proposed. However, learning precise boundary details from scribble annotations that lack edge information is still difficult. In this paper, we propose to learn precise boundaries from our designed synthetic images and labels without introducing any extra auxiliary data. The synthetic image creates boundary information by inserting synthetic concave regions that simulate the real concave regions of salient objects. Furthermore, we propose a novel self-consistent framework that consists of a global integral branch (GIB) and a boundary-aware branch (BAB) to train a saliency detector. GIB aims to identify integral salient objects, whose input is the original image. BAB aims to help predict accurate boundaries, whose input is the synthetic image. These two branches are connected through a self-consistent loss to guide the saliency detector to predict precise boundaries while identifying salient objects. Experimental results on five benchmarks demonstrate that our method outperforms the state-of-the-art weakly supervised SOD methods and further narrows the gap with the fully supervised methods.