Progressive Feature Polishing Network for Salient Object Detection


  • Bo Wang Zhejiang University
  • Quan Chen Alibaba Group
  • Min Zhou Alibaba Group
  • Zhiqiang Zhang Alibaba Group
  • Xiaogang Jin Zhejiang University
  • Kun Gai Alibaba Group



Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. By employing multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach is able to detect salient objects with fine details without any post-processing. A FPM parallelly updates the features of each level by directly incorporating all higher level context information. Moreover, it can keep the dimensions and hierarchical structures of the feature maps, which makes it flexible to be integrated with any CNN-based models. Empirical experiments show that our results are monotonically getting better with increasing number of FPMs. Without bells and whistles, PFPN outperforms the state-of-the-art methods significantly on five benchmark datasets under various evaluation metrics. Our code is available at:




How to Cite

Wang, B., Chen, Q., Zhou, M., Zhang, Z., Jin, X., & Gai, K. (2020). Progressive Feature Polishing Network for Salient Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12128-12135.



AAAI Technical Track: Vision