Semantic Segmentation from Image Labels by Reconstruction from Structured Decomposition
<|reference_start|>Semantic Segmentation from Image Labels by Reconstruction from Structured Decomposition: Weakly supervised image segmentation (WSSS) from image tags remains challenging due to its under-constraint nature. Most mainstream work focus on the extraction of class activation map (CAM) and imposing various additional regularization. Contrary to the mainstream, we propose to frame WSSS as a problem of reconstruction from decomposition of the image using its mask, under which most regularization are embedded implicitly within the framework of the new problem. Our approach has demonstrated promising results on initial experiments, and shown robustness against the problem of background ambiguity. Our code is available at \url{https://github.com/xuanrui-work/WSSSByRec}.<|reference_end|>
@article{zeng2024semantic,
    title={Semantic Segmentation from Image Labels by Reconstruction from
  Structured Decomposition},
    author={Xuanrui Zeng},
    journal={arXiv preprint arXiv:2409.01472},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01472},
    primaryClass={cs.CV}
}