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SimpleClick: Interactive Image Segmentation with Simple Vision Transformers.

Qin Liu¹, Zhenlin Xu¹, Gedas Bertasius¹

¹University of North Carolina at Chapel Hill.

Proceedings. IEEE International Conference on Computer Vision

|September 9, 2024

Summary

This summary is machine-generated.

SimpleClick is a novel interactive image segmentation method using a plain Vision Transformer (ViT) backbone. It achieves state-of-the-art results with fewer clicks, demonstrating broad applicability.

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Interactive image segmentation methods commonly use hierarchical backbones.
Plain Vision Transformers (ViTs) show promise for dense prediction tasks.
ViTs offer a foundation model approach adaptable to downstream tasks without backbone redesign.

Purpose of the Study:

Introduce SimpleClick, the first interactive segmentation method utilizing a plain backbone.
Explore the effectiveness of plain ViTs in interactive image segmentation.
Develop a method for efficient object extraction with minimal user interaction.

Main Methods:

Leveraged a plain, non-hierarchical Vision Transformer (ViT) backbone.
Introduced a symmetric patch embedding layer for click encoding.
Utilized a masked autoencoder (MAE) pretraining strategy for the ViT backbone.

Main Results:

Achieved state-of-the-art performance in interactive image segmentation.
Obtained 4.15 NoC@90 on the SBD dataset, a 21.8% improvement.
Demonstrated generalizability on medical imaging datasets.

Conclusions:

SimpleClick offers a highly effective and efficient approach to interactive image segmentation.
The method's performance and generalizability highlight its potential as a practical annotation tool.
Plain backbones are a viable and powerful alternative for interactive segmentation architectures.