Automatic Prompt Generation Using Class Activation Maps for Foundational Models: A Polyp Segmentation Case Study

Automatic Prompt Generation Using Class Activation Maps for Foundational Models: A Polyp Segmentation Case Study

We introduce a weakly supervised segmentation approach that leverages class activation maps and the Segment Anything Model to generate high-quality masks using only classification data. A pre-trained classifier produces class activation maps that, once thresholded, yield bounding boxes encapsulating...

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Bibliographic Details
Main Authors: Hanna Borgli, Håkon Kvale Stensland, Pål Halvorsen
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Machine Learning and Knowledge Extraction
Subjects:
image segmentation
class activation map
segment anything
annotation tools
zero-shot learning
weakly supervised semantic segmentation
Online Access:https://www.mdpi.com/2504-4990/7/1/22
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