# Explainable Artificial Intelligence: Advancements and Limitations
## 6 XAI and Weakly Supervised Semantic Segmentation
Contemporary approaches integrate box constraints into deep learning models, achieving better spatial precision by aligning predicted masks with bounding box edges and leveraging region-based loss functions [128, 129]. Scribbles and points serve as sparse spatial supervision, offering minimal yet explicit guidance about object locations. Scribble-based methods propagate annotations to unmarked regions using edge detection, graph-based propagation, or energy minimisation techniques [130, 131, 132]. Point-based methods rely on a few annotated pixels per object, often integrating these with background priors and unsupervised techniques to complete the segmentation [133]. A more recent methodology that gained attention is semantic proportion-based semantic segmentation (SPSS) [134], where only semantic proportions are utilised for semantic segmentation. Despite being several orders of magnitude cheaper than pixel-wise annotation in terms of annotation cost from both time and computational perspectives, SPSS is shown to be a promising WSSS methodology in several fields such as medical and aerial imaging, where obtaining full pixel annotation is challenging. Several studies have demonstrated the use of XAI outputs as weak supervision for semantic segmentation, effectively bridging the gap between limited annotations and high-quality segmentation models [135, 136]. For instance, HiResCAM-generated heatmaps have been used to refine annotations, enhancing segmentation models by highlighting relevant regions, particularly in complex image scenarios [137]. These approaches have been especially impactful in fields like medical imaging, where XAI-driven heatmaps help identify regions of interest for weakly supervised segmentation, reducing reliance on dense pixel-level annotations while improving model accuracy [138]. This integration of inter pretability and weak supervision demonstrates the dual benefit of enhanced transparency and reduced annotation efforts. Core approaches in WSSS revolve around generating and refining pseudo-labels, designing robust loss functions, and leveraging auxiliary information to improve segmentation accuracy. Loss functions are tailored to handle weak supervision, employing strategies to encourage confident predictions, and consistency regularisation to ensure stability under perturbations. Additionally, auxiliary cues, like saliency maps and edge detectors, help strengthen spatial information and guide model training. The evaluation of WSSS methods relies on benchmark datasets and performance metrics that capture segmentation quality. Popular datasets, such as PASCAL VOC [139], MS COCO [140], and Cityscapes [141], provide a range of challenges in terms of object diversity, scale, and complexity. These datasets often include subsets tailored for weak supervision, with annotations like image-level labels or bounding boxes. The primary metric for assessing WSSS methods is Intersection over Union (IoU), which quantifies the overlap between predicted and ground-truth masks. To complement IoU, metrics like boundary accuracy and object localisation precision are sometimes used to provide finer insights into model performance. As WSSS research advances, the development of more diverse datasets and evaluation criteria will play a crucial role in driving progress and ensuring the robustness of these methods in real-world scenarios. WSSS has emerged as a promising approach to address the high annotation costs associated with fully supervised methods, providing high-quality segmentation with minimal supervision. By utilising diverse forms of weak annotations, WSSS effectively balances annotation efficiency with segmentation performance. However, it still faces significant challenges such as localisation bias, where methods like CAM focus on the most discriminative object parts while neglecting less salient regions and noise in pseudo-labels, which can propagate errors during training.