  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.
