@misc{17961,
  author = {Hanna Borgli and H{\r a}kon Stensland and P{\r a}l Halvorsen},
  title = {Better Image Segmentation with Classification: Guiding Zero-Shot Models Using Class Activation Maps},
  abstract = {In this demo paper, we present a method for image segmentation of images with no segmentation masks available and a web application built on top of it. The method does not require any segmentation images for training; instead, it uses a classifier trained on classification images to power a zero-shot method to get the segmentation masks. We focus on gastrointestinal images and include datasets with both ground truth masks available and not available. In this demo, the user is shown the steps for generating the mask and can observe where the method excels and is challenged. We also allow the users to experiment with parameters for the generation but have provided default parameters based on experiments. With this system, users can get masks for any object for which classification data is available. They can use it to test the viability of creating hand-crafted masks for training, automatically annotate datasets, and deploy it as-is. A video demonstration can be found at https://youtu.be/YjX19bBXf6Y.},
  year = {2025},
  journal = {International Conference on Multimedia Modeling (MMM)},
  pages = {105-111},
  month = {01/2025},
  publisher = {Springer},
  isbn = {978-981-96-2073-9},
  url = {https://link.springer.com/chapter/10.1007/978-981-96-2074-6_10},
  doi = {https://doi.org/10.1007/978-981-96-2074-6_10},
}