Weakly Supervised Semantic Segmentation Network with Multiple Boundary References

In response to the challenges of object occlusion and overlap in weakly supervised semantic segmentation tasks, we propose a multi-boundary-reference weakly supervised semantic segmentation network. The network incorporates a boundary exploration module, which focuses on integrating spatial-domain and frequency-domain boundaries and obtains object region boundaries through mutual referencing between spatial and frequency domains. This enables the network to accurately locate objects even in cases of overlap or occlusion. Subsequently, the obtained object region boundaries are inputted into the network as reference information, allowing the network to generate pseudo pixel masks that more accurately cover the target region. This, in turn, enhances the network’s segmentation accuracy in scenarios of object overlap or occlusion. Experimental validation and analysis conducted on the widely used datasets PASCAL VOC 2012 and COCO 2014 demonstrate that the proposed network achieves mIoU values of 69.2% and 40.6%, respectively. Furthermore, it effectively addresses the issue of low segmentation accuracy in cases of object occlusion or overlap, thereby improving the accuracy of weakly supervised semantic segmentation.