State-of-the-art techniques in weakly-supervised semantic segmentation (WSSS) using image-level labels exhibit severe performance degradation on driving scene datasets such as Cityscapes. To address this challenge, we develop a new WSSS framework tailored to driving scene datasets. Based on extensive analysis of dataset characteristics, we employ Contrastive Language-Image Pre-training (CLIP) as our baseline to obtain pseudo-masks. However, CLIP introduces two key challenges: (1) pseudo-masks from CLIP lack in representing small object classes, and (2) these masks contain notable noise. We propose solutions for each issue as follows. (1) We devise Global-Local View Training that seamlessly incorporates small-scale patches during model training, thereby enhancing the model's capability to handle small-sized yet critical objects in driving scenes (e.g., traffic light). (2) We introduce Consistency-Aware Region Balancing (CARB), a novel technique that discerns reliable and noisy regions through evaluating the consistency between CLIP masks and segmentation predictions. It prioritizes reliable pixels over noisy pixels via adaptive loss weighting. Notably, the proposed method achieves 51.8\% mIoU on the Cityscapes test dataset, showcasing its potential as a strong WSSS baseline on driving scene datasets. Experimental results on CamVid and WildDash2 demonstrate the effectiveness of our method across diverse datasets, even with small-scale datasets or visually challenging conditions. The code is available at https://github.com/k0u-id/CARB.

翻译：当前基于图像级标签的弱监督语义分割技术在驾驶场景数据集（如Cityscapes）上表现出显著的性能退化。为应对这一挑战，我们开发了一套专为驾驶场景数据集设计的弱监督语义分割框架。基于对数据集特征的深入分析，我们采用对比语言-图像预训练技术作为基线来获取伪掩码。然而，CLIP引入两个关键问题：（1）CLIP生成的伪掩码难以表征小目标类别，（2）这些掩码包含明显的噪声。我们针对每个问题提出如下解决方案：（1）设计全局-局部视图训练方法，在模型训练中无缝融入小尺度图像块，从而增强模型处理驾驶场景中关键小目标（如交通灯）的能力；（2）提出一致性感知区域平衡技术，通过评估CLIP掩码与分割预测之间的一致性来区分可靠区域与噪声区域，并利用自适应损失权重优先处理可靠像素。值得注意的是，所提方法在Cityscapes测试集上达到51.8%的mIoU，展现了作为驾驶场景数据集强基线方法的潜力。在CamVid和WildDash2上的实验结果验证了该方法在多种数据集（包括小规模数据集或视觉挑战性场景）中的有效性。代码已开源：https://github.com/k0u-id/CARB。