通过跨架构一致性正则化缓解弱监督语义分割中的虚假相关性
Mitigating Spurious Correlations in Weakly Supervised Semantic Segmentation via Cross-architecture Consistency Regularization
AI总结：
针对弱监督语义分割中因图像级标签训练导致的虚假相关性等问题，提出结合CNN与ViT的师生框架，通过知识迁移损失实现跨架构一致性，辅以处理提升伪掩码质量。
摘要：
像素级标签的稀缺是实际场景中的一大挑战。在工业烟气等特定领域，获取此类精细标注尤为困难，且通常需要专业知识。为缓解这一问题，弱监督语义分割（WSSS）已成为一种颇具前景的方法。然而，由于监督信号的差距以及仅用图像级标签训练的模型存在固有偏差，现有WSSS方法存在前景覆盖不完整、目标边界不准确以及虚假相关性等局限，在我们的研究领域中尤为突出——排放物总是与烟囱在空间上耦合在一起。\n以往的解决方案通常依赖额外先验或外部知识来缓解这些问题，但它们往往缺乏可扩展性，且无法解决模型对共现上下文的固有偏差。针对这一问题，我们提出了一种新型WSSS框架，无需依赖外部监督即可直接解决共现问题。与采用单一网络的现有方法不同，我们使用结合了CNN（卷积神经网络）和ViT（视觉Transformer）的师生框架。我们引入了一种知识迁移损失，通过对齐内部表征来强制实现跨架构一致性。此外，我们还结合了后处理技术以解决部分覆盖问题，进一步提升伪掩码质量。
英文摘要：
Scarcity of pixel-level labels is a significant challenge in practical scenarios. In specific domains like industrial smoke, acquiring such detailed annotations is particularly difficult and often requires expert knowledge. To alleviate this, weakly supervised semantic segmentation (WSSS) has emerged as a promising approach. However, due to the supervision gap and inherent bias in models trained with only image level labels, existing WSSS methods suffer from limitations such as incomplete foreground coverage, inaccurate object boundaries, and spurious correlations, especially in our domain, where emissions are always spatially coupled with chimneys. Previous solutions typically rely on additional priors or external knowledge to mitigate these issues, but they often lack scalability and fail to address the model's inherent bias toward co-occurring context. To address this, we propose a novel WSSS framework that directly targets the co-occurrence problem without relying on external supervision. Unlike prior methods that adopt a single network, we employ a teacher-student framework that combines CNNs and ViTs. We introduce a knowledge transfer loss that enforces cross-architecture consistency by aligning internal representations. Additionally, we incorporate post-processing techniques to address partial coverage and further improve pseudo mask quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
