PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation

TL;DR

This paper introduces Panoptic-PartFormer++, a unified end-to-end framework for panoptic part segmentation that improves accuracy and proposes a new metric, PWQ, to better evaluate the task.

Contribution

It presents the first end-to-end unified architecture for PPS, a new metric PWQ, and an enhanced model Panoptic-PartFormer++ with cross-attention for better segmentation.

Findings

Achieves 2% PartPQ and 3% PWQ improvements on Cityscapes PPS dataset.

Achieves 5% PartPQ improvement on Pascal Context PPS dataset.

Sets new state-of-the-art results in PPS tasks.

Abstract

Panoptic Part Segmentation (PPS) unifies panoptic and part segmentation into one task. Previous works utilize separate approaches to handle things, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework, Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we first design a meta-architecture that decouples part features and things/stuff features, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Second, we propose a new metric Part-Whole Quality (PWQ), better to measure this task from pixel-region and part-whole perspectives. It also decouples the errors for part segmentation and panoptic segmentation. Third, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross-attention scheme to boost part segmentation qualities further. We design a new part-whole interaction method using masked cross attention. Finally, extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results. Our models can serve as a strong baseline and aid future research in PPS. The source code and trained models will be available at~\url{https://github.com/lxtGH/Panoptic-PartFormer}.