Authors: Jonas Herzog

Published on: February 27, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2402.17614

Summary

• What is new: Eliminated the training stage and the main segmentation network, using test-time task-adaptation for cross-domain few-shot segmentation.
• Why this is important: Few-shot segmentation performance drops significantly when applied to images outside the training domain.
• What the research proposes: Appending small networks to a pre-trained backbone’s feature pyramid, using consistency across augmented views for learning without training on multiple images.
• Results: Achieved state-of-the-art performance in cross-domain few-shot segmentation without using any images other than a few labeled samples at test time.

Technical Details

Technological frameworks used: Feature pyramid augmentation with small network appendages

Models used: Classification-pretrained backbones

Data used: Few labeled samples and augmented views

Potential Impact

Industries relying on image segmentation, such as medical imaging, autonomous vehicles, and surveillance.

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