# Explainable Artificial Intelligence: Advancements and Limitations
## 6 XAI and Weakly Supervised Semantic Segmentation  
Semantic segmentation is a core task in computer vision, focused on assigning semantically meaningful labels to every pixel in an image to identify specific objects. This task has diverse applications, including autonomous driving [115; 116], scene understanding [117], and medical image analysis [118; 119]. By enabling machines to extract detailed semantic information from images, it brings them closer to human-like visual perception. However, the inherent complexity and variability of real-world scenes, combined with the substantial need for labelled data to train deep learning models, make semantic segmentation a challenging problem. To tackle these difficulties, researchers have proposed various methods, such as fully convolutional networks (FCNs) [120], encoder-decoder architectures [121; 122], and attention mechanisms [123]. These approaches have driven significant progress in semantic segmentation, establishing it as a vibrant and rapidly evolving research field. Despite these architectural advancements, the requirement of pixel-wise segmentation maps remains a significant challenge for successful semantic segmentation applications. WSSS (weakly supervised semantic segmentation) approaches have been proposed to address this expensive per-pixel annotation challenge by leveraging less detailed coarse annotations, such as image-level labels, bounding boxes, scribbles, or points, to significantly reduce annotation efforts. Depending on the granularity and type of available annotations, weak supervision can be categorised into these types, each offering unique advantages and challenges that influence the methods and performance of WSSS approaches. XAI has also emerged as a promising avenue for providing weak supervision in this context, offering significant potential for reducing reliance on fully annotated data. Techniques like Grad-CAM [80], LRP [68], and Integrated Gradients [74] generate saliency maps that highlight key regions contributing to a model's predictions, making them valuable for WSSS. These outputs can serve as pseudo-annotations, guiding segmentation models by localising salient object parts. Hybrid approaches that combine XAI-generated pseudo-labels with weak annotations, such as image-level labels or bounding boxes, further enhance segmentation accuracy while minimising annotation costs. By aligning model interpretability with segmentation tasks, XAI fosters more transparent and explainable WSSS pipelines, bridging the gap between coarse annotations and precise segmentation, and ultimately reducing the need for dense supervision. Below, we explore methodologies that utilise various levels of annotations, ranging from image-level labels to saliency maps generated by XAI techniques, as forms of weak supervision for semantic segmentation tasks. These methodologies were shown to be effective in outputting segmentation maps while keeping the annotation cost relatively low. Image-level labels indicate the presence of classes in an image without providing spatial information. They are the weakest form of supervision as they lack localisation details. Methods using image-level labels often begin with CAM to localise discriminative regions associated with each class. While CAM-based approaches are computationally efficient, they tend to focus on the most salient parts of an object, leading to incomplete segmentations. Recent advancements aim to overcome these limitations through seed refinement, self-training, and affinity propagation, enabling broader and more accurate object coverage [124, 125, 126]. Bounding box annotations provide a coarse spatial indication of object locations, offering a balance between annotation cost and localisation precision. Early methods like BoxSup [127] refine segmentations iteratively within the boundaries defined by the boxes.