Pseudo-label learning is widely used in semantic segmentation, particularly in label-scarce scenarios such as unsupervised domain adaptation (UDA) and semisupervised learning (SSL). Despite its success, this paradigm can generate erroneous pseudo-labels, which are further amplified during training due to utilization of one-hot encoding. To address this issue, we propose ECOCSeg, a novel perspective for segmentation models that utilizes error-correcting output codes (ECOC) to create a fine-grained encoding for each class. ECOCSeg offers several advantages. First, an ECOC-based classifier is introduced, enabling model to disentangle classes into attributes and handle partial inaccurate bits, improving stability and generalization in pseudo-label learning. Second, a bit-level label denoising mechanism is developed to generate higher-quality pseudo-labels, providing adequate and robust supervision for unlabeled images. ECOCSeg can be easily integrated with existing methods and consistently demonstrates significant improvements on multiple UDA and SSL benchmarks across different segmentation architectures. Code is available at https://github.com/Woof6/ECOCSeg.

翻译：伪标签学习在语义分割领域被广泛应用，特别是在标签稀缺的场景中，如无监督域适应（UDA）和半监督学习（SSL）。尽管取得了成功，该范式可能生成错误的伪标签，而独热编码（one-hot encoding）的使用会在训练过程中进一步放大这些错误。为解决此问题，我们提出了ECOCSeg，这是一种用于分割模型的新颖视角，它利用纠错输出码（ECOC）为每个类别创建细粒度编码。ECOCSeg具有多个优势。首先，我们引入了基于ECOC的分类器，使模型能够将类别解耦为属性并处理部分不准确的比特位，从而提高了伪标签学习的稳定性和泛化能力。其次，我们开发了一种比特级标签去噪机制，以生成更高质量的伪标签，为未标记图像提供充分且鲁棒的监督。ECOCSeg可以轻松地与现有方法集成，并在多种UDA和SSL基准测试中，跨越不同的分割架构，持续展现出显著的性能提升。代码可在 https://github.com/Woof6/ECOCSeg 获取。