From Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation

TL;DR

This paper presents a hierarchical transformer model that improves image segmentation by combining local and global aggregation strategies to effectively segment parts and objects, achieving state-of-the-art results.

Contribution

A novel hierarchical transformer-based approach that integrates local and global aggregation for enhanced part and object segmentation performance.

Findings

Outperforms previous methods on PartImageNet with +2.8% and +0.8% mIoU improvements.

Achieves +1.5% and +2.0% mIoU improvements on Pascal Part dataset.

Effectively balances supervision modalities and computational efficiency.

Abstract

In this paper, we introduce a hierarchical transformer-based model designed for sophisticated image segmentation tasks, effectively bridging the granularity of part segmentation with the comprehensive scope of object segmentation. At the heart of our approach is a multi-level representation strategy, which systematically advances from individual pixels to superpixels, and ultimately to cohesive group formations. This architecture is underpinned by two pivotal aggregation strategies: local aggregation and global aggregation. Local aggregation is employed to form superpixels, leveraging the inherent redundancy of the image data to produce segments closely aligned with specific parts of the object, guided by object-level supervision. In contrast, global aggregation interlinks these superpixels, organizing them into larger groups that correlate with entire objects and benefit from part-level supervision. This dual aggregation framework ensures a versatile adaptation to varying supervision inputs while maintaining computational efficiency. Our methodology notably improves the balance between adaptability across different supervision modalities and computational manageability, culminating in significant enhancement in segmentation performance. When tested on the PartImageNet dataset, our model achieves a substantial increase, outperforming the previous state-of-the-art by 2.8% and 0.8% in mIoU scores for part and object segmentation, respectively. Similarly, on the Pascal Part dataset, it records performance enhancements of 1.5% and 2.0% for part and object segmentation, respectively.