IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel Relation
<|reference_start|>IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel Relation: The scarcity of labeled data in real-world scenarios is a critical bottleneck of deep learning's effectiveness. Semi-supervised semantic segmentation has been a typical solution to achieve a desirable tradeoff between annotation cost and segmentation performance. However, previous approaches, whether based on consistency regularization or self-training, tend to neglect the contextual knowledge embedded within inter-pixel relations. This negligence leads to suboptimal performance and limited generalization. In this paper, we propose a novel approach IPixMatch designed to mine the neglected but valuable Inter-Pixel information for semi-supervised learning. Specifically, IPixMatch is constructed as an extension of the standard teacher-student network, incorporating additional loss terms to capture inter-pixel relations. It shines in low-data regimes by efficiently leveraging the limited labeled data and extracting maximum utility from the available unlabeled data. Furthermore, IPixMatch can be integrated seamlessly into most teacher-student frameworks without the need of model modification or adding additional components. Our straightforward IPixMatch method demonstrates consistent performance improvements across various benchmark datasets under different partitioning protocols.<|reference_end|>
@article{wu2024ipixmatch:,
    title={IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel
  Relation},
    author={Kebin Wu, Wenbin Li, Xiaofei Xiao},
    journal={arXiv preprint arXiv:2404.18891},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18891},
    primaryClass={cs.CV cs.AI cs.LG}
}