Salient object detection with image-level binary supervision

作者：

Highlights：

• We propose a learning framework for salient object detection, which is trained only on image-level binary labels but yields comparable performance to state-of-the-art weakly-supervised methods.

• We propose a target label hallucination method, which can synthesize quality pseudo ground truth saliency maps from just binary labels.

• We have conducted extensive experimental evaluation to demonstrate the effectiveness of the proposed weakly-supervised method.

摘要

•We propose a learning framework for salient object detection, which is trained only on image-level binary labels but yields comparable performance to state-of-the-art weakly-supervised methods.•We propose a target label hallucination method, which can synthesize quality pseudo ground truth saliency maps from just binary labels.•We have conducted extensive experimental evaluation to demonstrate the effectiveness of the proposed weakly-supervised method.

论文关键词：Weak supervision,Salient object detection,Binary labels

论文评审过程：Received 22 September 2020, Revised 18 April 2022, Accepted 7 May 2022, Available online 10 May 2022, Version of Record 14 May 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108782