## 2 Related Work
### Weakly Supervised Learning  
Image segmentation is a technique introduced to distinguish between objects and backgrounds or delineate boundaries within an image [21]. It plays a vital role in computer vision and finds applications across various fields. Traditionally, segmentation methods have primarily utilized region-based approaches [22]. These methods typically segment images based on regional similarities. Key techniques include thresholding, which determines a threshold by analyzing pixel value distributions to binarize images for segmentation; region growing, which begins with initial seed pixels and expands to include neighboring pixels sharing similar characteristics; and split and merge, which divides the image into uniform regions and then combines adjacent regions exhibiting similar properties. Various deep learning-based methods have been applied in the field of image segmentation. For example, fully convolutional networks (FCNs) [23] are crafted to execute pixel-level classification by altering the traditional CNN architecture; they replace fully connected layers with convolutional ones to preserve the input image's resolution while assessing class probabilities for each pixel. U-Net [24], characterized by its U-shaped architecture, proves especially effective for medical imaging tasks due to its symmetrical encoder-decoder structure, which facilitates precise pixel-wise segmentation by concurrently capturing fine-grained details and high-level characteristics. Additionally, DeepLab [25] incorporates Atrous Convolution and Pyramid Pooling to apprehend features at various scales, whilemaintaining the spatial resolution, rendering it highly effective in segmenting complex scenes where distinguishing between objects and backgrounds poses a challenge. These deep learning models enable accurate pixel-wise segmentation, evidencing high performance across diverse applications such as medical image analysis, autonomous driving, and object recognition. Contrary to traditional image segmentation techniques, weakly supervised segmentation (WSSS) can effectively segment images without requiring precise pixel-level labeling. WSSS utilizes limited label information (e.g., image-level labels or bounding boxes) to conduct segmentation, thus reducing the costs and time associated with data labeling while potentially enhancing performance. Since it does not depend on pixel-level labels, WSSS is particularly valuable in industrial settings where the prevalence of large-scale unlabeled image data is common, offering a promising approach for training models on such datasets. [PERSON] et al. [26] utilize class activation maps (CAMs), which generate an initial segmentation mask based on the feature maps from the final convolutional layer of the image classification model and the class-specific learned weights. They then refine this initial mask through a method termed re-activation maps for weakly supervised semantic segmentation (ReCAM). [PERSON] et al. [27] used saliency maps to train an initial segmentation model and progressively enhanced the model's performance by re-training it with the predicted results of previous models on increasingly complex images. [PERSON] et al. [28] introduced BoxSup, a novel method leveraging bounding box annotations rather than segmentation masks to produce segmentation outcomes. This approach iteratively refines segmentation masks utilizing region proposals and CNN training, progressively enhancing network performance. Similarly, [PERSON] et al. [29] utilized a recursive training methodology based on DeepLabv2, incorporating advanced GrabCut+, dense-CRF, and MCG post-processing techniques. They achieved roughly 95% of the results of fully supervised methods, using only box annotations on the VOC12 and COCO datasets. This study employs a WSSS technique that uses bounding boxes and iterative learning to gradually enhance the labeling process [30].