Abstract
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 this https URL.
Abstract (translated)
伪标签学习在语义分割中广泛应用，特别是在标记稀缺的情况下，如无监督领域自适应（UDA）和半监督学习（SSL）。尽管这种方法取得了成功，但它可能会生成错误的伪标签，在训练过程中由于使用了一_hot编码而进一步放大了这些错误。为了解决这一问题，我们提出了ECOCSeg——一种新的语义分割模型视角，利用纠错输出码（ECOC）为每个类别创建精细的编码。 ECOCSeg具有几大优势： 1. 引入了一个基于ECOC的分类器，使模型能够将类别分解为属性，并处理部分不准确的信息位。这提高了伪标签学习过程中的稳定性和泛化能力。 2. 开发了一种位级标签去噪机制，用于生成更高质量的伪标签，这些标签能为未标记图像提供充分且稳健的监督。 ECOCSeg可以轻松地与现有方法结合，并在多个UDA和SSL基准测试中对不同的分割架构表现出持续显著的改进。代码可在此链接获取：[此链接应替换为你提供的具体URL]。
URL
https://arxiv.org/abs/2512.06870
