Rethinking the U-Shape Structure for Salient Object Detection.

Area of Science:

• Computer Vision
• Artificial Intelligence

Background:

• U-shape architectures are effective for salient object detection by combining multi-scale features.
• Existing methods often overlook the crucial connections between the bottom-up and top-down pathways in U-shape networks.

Purpose of the Study:

• To enhance salient object detection by improving cross-scale information interaction within U-shape networks.
• To develop a novel strategy that centralizes connections between pathways for stronger, more precise features.

Main Methods:

• Proposed a strategy to centralize connections between bottom-up and top-down pathways.
• Introduced a relative global calibration module for simultaneous multi-scale input processing without spatial interpolation.
• Integrated the approach into existing U-shape salient object detection methods.

Main Results:

• Achieved semantically stronger and positionally more precise features.
• Demonstrated superior performance against state-of-the-art methods on five benchmarks.
• Showcased effectiveness with minimal additional parameters and reduced computational complexity.

Conclusions:

• Centralizing pathway connections in U-shape networks significantly enhances salient object detection.
• The proposed relative global calibration module offers an efficient way to aggregate multi-scale features.
• The method is versatile and improves various existing U-shape salient object detection models.