Reproducing & Enhancing Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation
Abstract
Recent advancements in semi-supervised semantic segmentation (SSS) have gained significant attention due to their potential to improve segmentation accuracy while reducing the reliance on large annotated datasets. These methods have shown promising results, but often at the expense of increasing complexity in model design and training procedures. To address this, Zhao et al. proposed a novel approach called AugSeg, which prioritizes simplicity and efficiency in enhancing SSS performance. The application of SSS is crucial in various fields such as medical imaging, autonomous driving, and satellite imagery analysis, where accurate segmentation of objects or regions of interest is essential for decision-making processes. Traditional supervised semantic segmentation methods require extensive manual annotation of training data, which is time-consuming and costly. In contrast, SSS methods leverage both labeled and unlabeled data, offering a more cost-effective solution for training segmentation models. AugSeg deviates from the current trend of complex SSS approaches by focusing on data perturbations to boost performance. By simplifying data augmentation techniques and adopting an adaptive approach to inject labeled information into unlabeled samples, AugSeg aims to achieve state-of-the-art performance on SSS benchmarks while maintaining simplicity in design and implementation.