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ClickAttention: Click region similarity guided interactive segmentation.

Long Xu¹, Yongquan Chen², Shanghong Li²

¹Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen, 518172, China; Guangxi Medical University, Nanning, 530021, China.

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Summary

This summary is machine-generated.

This study introduces a novel click attention algorithm for interactive image segmentation. The method enhances positive click influence and reduces interference, achieving superior performance with fewer parameters.

Keywords:

Attention coupling Click attention Discriminative affinity loss Interactive segmentation

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Interactive segmentation relies on user clicks, but current methods struggle with local focus and efficiency.
Existing algorithms often require numerous clicks and face challenges in balancing performance and computational cost.

Purpose of the Study:

To develop an efficient interactive segmentation algorithm that expands click influence and minimizes interference.
To improve segmentation accuracy and reduce parameter count compared to state-of-the-art methods.

Main Methods:

Proposed a click attention algorithm that leverages region similarity to broaden positive click impact.
Introduced a discriminative affinity loss to decouple positive and negative click attention, preventing accuracy loss.
Evaluated the method on the DAVIS dataset.

Main Results:

Achieved a 2% performance gain (NoC@90) over SimpleClick-ViT-L on the DAVIS dataset.
Reduced parameter usage to only 15.6% of the state-of-the-art method.
Demonstrated superior performance and efficiency compared to existing interactive segmentation techniques.

Conclusions:

The proposed click attention algorithm offers state-of-the-art performance in interactive segmentation with significantly fewer parameters.
The method effectively addresses limitations of existing approaches, providing a more efficient and accurate solution.
Published data and code facilitate further research and application in image segmentation.