A Bottom-Up Approach to Class-Agnostic Image Segmentation
<|reference_start|>A Bottom-Up Approach to Class-Agnostic Image Segmentation: Class-agnostic image segmentation is a crucial component in automating image editing workflows, especially in contexts where object selection traditionally involves interactive tools. Existing methods in the literature often adhere to top-down formulations, following the paradigm of class-based approaches, where object detection precedes per-object segmentation. In this work, we present a novel bottom-up formulation for addressing the class-agnostic segmentation problem. We supervise our network directly on the projective sphere of its feature space, employing losses inspired by metric learning literature as well as losses defined in a novel segmentation-space representation. The segmentation results are obtained through a straightforward mean-shift clustering of the estimated features. Our bottom-up formulation exhibits exceptional generalization capability, even when trained on datasets designed for class-based segmentation. We further showcase the effectiveness of our generic approach by addressing the challenging task of cell and nucleus segmentation. We believe that our bottom-up formulation will offer valuable insights into diverse segmentation challenges in the literature.<|reference_end|>
@article{dille2024a,
    title={A Bottom-Up Approach to Class-Agnostic Image Segmentation},
    author={Sebastian Dille and Ari Blondal and Sylvain Paris and Yau{g}{i}z
  Aksoy},
    journal={arXiv preprint arXiv:2409.13687},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13687},
    primaryClass={cs.CV}
}