Seg-HGNN: Unsupervised and Light-Weight Image Segmentation with Hyperbolic Graph Neural Networks
<|reference_start|>Seg-HGNN: Unsupervised and Light-Weight Image Segmentation with Hyperbolic Graph Neural Networks: Image analysis in the euclidean space through linear hyperspaces is well studied. However, in the quest for more effective image representations, we turn to hyperbolic manifolds. They provide a compelling alternative to capture complex hierarchical relationships in images with remarkably small dimensionality. To demonstrate hyperbolic embeddings' competence, we introduce a light-weight hyperbolic graph neural network for image segmentation, encompassing patch-level features in a very small embedding size. Our solution, Seg-HGNN, surpasses the current best unsupervised method by 2.5\%, 4\% on VOC-07, VOC-12 for localization, and by 0.8\%, 1.3\% on CUB-200, ECSSD for segmentation, respectively. With less than 7.5k trainable parameters, Seg-HGNN delivers effective and fast ($\approx 2$ images/second) results on very standard GPUs like the GTX1650. This empirical evaluation presents compelling evidence of the efficacy and potential of hyperbolic representations for vision tasks.<|reference_end|>
@article{mondal2024seg-hgnn:,
    title={Seg-HGNN: Unsupervised and Light-Weight Image Segmentation with
  Hyperbolic Graph Neural Networks},
    author={Debjyoti Mondal and Rahul Mishra and Chandan Pandey},
    journal={arXiv preprint arXiv:2409.06589},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06589},
    primaryClass={cs.CV}
}