Is Depth Really Necessary for Salient Object Detection?

TL;DR

This paper introduces a novel depth-aware salient object detection framework that trains with depth data but only requires RGB input during testing, achieving superior performance over existing RGB and RGBD methods.

Contribution

It is the first to propose a unified depth-aware SOD framework that uses depth information only during training, enhancing accuracy with less during inference.

Findings

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

Surpasses RGBD-based methods by a large margin with less information.

Uses depth as a regularization and error correction during training.

Abstract

Salient object detection (SOD) is a crucial and preliminary task for many computer vision applications, which have made progress with deep CNNs. Most of the existing methods mainly rely on the RGB information to distinguish the salient objects, which faces difficulties in some complex scenarios. To solve this, many recent RGBD-based networks are proposed by adopting the depth map as an independent input and fuse the features with RGB information. Taking the advantages of RGB and RGBD methods, we propose a novel depth-aware salient object detection framework, which has following superior designs: 1) It only takes the depth information as training data while only relies on RGB information in the testing phase. 2) It comprehensively optimizes SOD features with multi-level depth-aware regularizations. 3) The depth information also serves as error-weighted map to correct the segmentation process. With these insightful designs combined, we make the first attempt in realizing an unified depth-aware framework with only RGB information as input for inference, which not only surpasses the state-of-the-art performances on five public RGB SOD benchmarks, but also surpasses the RGBD-based methods on five benchmarks by a large margin, while adopting less information and implementation light-weighted. The code and model will be publicly available.