# JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation

arXiv: https://arxiv.org/abs/2311.18618 · HTML: https://mlsanity.com/paper/2311.18618/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 66 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 30 of 53)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Panoptic-DeepLab](https://mlsanity.com/paper/1910.04751/) (2019)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (2019)
- [UPSNet: A Unified Panoptic Segmentation Network](https://mlsanity.com/paper/1901.03784/) (2019)
- [IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments](https://mlsanity.com/paper/1811.10200/) (2018)
- [Simultaneous Detection and Segmentation](https://mlsanity.com/paper/1407.1808/) (2014)
- [Instance-level Human Parsing via Part Grouping Network](https://mlsanity.com/paper/1808.00157/) (2018)
- [Hypercolumns for Object Segmentation and Fine-grained Localization](https://mlsanity.com/paper/1411.5752/) (2014)
- [Fully Convolutional Instance-aware Semantic Segmentation](https://mlsanity.com/paper/1611.07709/) (2016)
- [Seamless Scene Segmentation](https://mlsanity.com/paper/1905.01220/) (2019)
- [EfficientPS: Efficient Panoptic Segmentation](https://mlsanity.com/paper/2004.02307/) (2020)
- [Searching for Efficient Multi-Scale Architectures for Dense Image Prediction](https://mlsanity.com/paper/1809.04184/) (2018)
- [Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation](https://mlsanity.com/paper/1503.00848/) (2015)
- [SSAP: Single-Shot Instance Segmentation With Affinity Pyramid](https://mlsanity.com/paper/1909.01616/) (2019)

## Built on by (most cited first, 2 of 2)

- [LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation](https://mlsanity.com/paper/2510.25263/) (2025)
- [Dictionary-based Framework for Interpretable and Consistent Object Parsing](https://mlsanity.com/paper/2502.19540/) (2025)

## Abstract (arXiv)

Part-aware panoptic segmentation is a problem of computer vision that aims to provide a semantic understanding of the scene at multiple levels of granularity. More precisely, semantic areas, object instances, and semantic parts are predicted simultaneously. In this paper, we present our Joint Panoptic Part Fusion (JPPF) that combines the three individual segmentations effectively to obtain a panoptic-part segmentation. Two aspects are of utmost importance for this: First, a unified model for the three problems is desired that allows for mutually improved and consistent representation learning. Second, balancing the combination so that it gives equal importance to all individual results during fusion. Our proposed JPPF is parameter-free and dynamically balances its input. The method is evaluated and compared on the Cityscapes Panoptic Parts (CPP) and Pascal Panoptic Parts (PPP) datasets in terms of PartPQ and Part-Whole Quality (PWQ). In extensive experiments, we verify the importance of our fair fusion, highlight its most significant impact for areas that can be further segmented into parts, and demonstrate the generalization capabilities of our design without fine-tuning on 5 additional datasets.