Edge Distraction-aware Salient Object Detection

Sucheng Ren, Wenxi Liu, Jianbo Jiao, Guoqiang Han, Shengfeng He

Research output: Contribution to journalArticlepeer-review


Integrating low-level edge features has been proven to be effective in preserving clear boundaries of salient objects. However, the locality of edge features makes it difficult to capture globally salient edges, leading to distraction in the final predictions. To address this problem, we propose to produce distraction-free edge features by incorporating cross-scale holistic interdependencies between high-level features. In particular, we first formulate our edge features extraction process as a boundary-filling problem. In this way, we enforce edge features to focus on closed boundaries instead of those disconnected background edges. Secondly, we propose to explore cross-scale holistic contextual connections between every position pair of high-level feature maps regardless of their distances across different scales. It selectively aggregates features at each position based on its connections to all the others, simulating the “contrast” stimulus of visual saliency. Finally, we present a complementary features integration module to fuse low- and high-level features according to their properties. Experimental results demonstrate our proposed method outperforms previous state-of-the-art methods on the benchmark datasets, with the fast inference speed of 30 FPS on a single GPU.

Original languageEnglish
JournalIEEE Multimedia
Early online date10 Jan 2023
Publication statusE-pub ahead of print - 10 Jan 2023

Bibliographical note

Publisher Copyright:


  • Convolution
  • Feature extraction
  • Filling
  • Image edge detection
  • Object detection
  • Task analysis
  • Visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Media Technology
  • Hardware and Architecture
  • Computer Science Applications


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