We present a hierarchical transformer-based model for image segmentation that effectively links the segmentation of detailed parts to the broader context of object segmentation. Central to our approach is a multi-level representation, progressing from individual pixels to superpixels, and finally to cohesive groups. This progression is characterized by two key aggregation strategies: local aggregation for forming superpixels and global aggregation for clustering these superpixels into group tokens. The formation of superpixels through local aggregation taps into the redundancy of image data, yielding segments that align with image parts under object-level supervision. Conversely, the global aggregation process assembles these superpixels into groups that demonstrate a tendency to align with whole objects, especially when guided by part-level supervision. This methodology achieves an optimal balance between adaptability to different types of supervision and computational efficiency, leading to notable advancements in the segmentation of both parts and objects. When evaluated on the PartImageNet dataset, our approach surpasses the previous state-of-the-art by 2.8% and 0.8% in part and object mIoU scores, respectively. Similarly, on the Pascal Part dataset, it demonstrates improvements of 1.5% and 2.0% for part and object mIoU, respectively.