Source-linked AI summary
An End-to-End Network for Panoptic Segmentation
Huanyu Liu, Chao Peng, Changqian Yu, Jingbo Wang, Xu Liu, Gang Yu, Wei Jiang
TL;DR
Panoptic segmentation must label every pixel and separate object instances, while traditional systems use inefficient independent models and heuristic merging. This paper introduces a shared end-to-end network with spatial ranking for instance overlap, and reports state-of-the-art performance on COCO panoptic segmentation.
Problem
Traditional panoptic segmentation uses independent instance and stuff models without shared features and heuristic merging, making the pipeline inefficient and complicating overlapping-instance resolution.
Method
The paper combines instance and stuff segmentation in one network with shared backbone features, task-specific heads, and a spatial ranking module for instance ordering.
Results
The approach outperforms previous state-of-the-art models and achieves state-of-the-art performance on the COCO panoptic segmentation dataset.
Takeaways & Limitations
A single occlusion-aware pipeline can jointly predict instance and stuff segmentation while reducing separate-model computation and heuristic merging.
Abstract
from arXiv · showhide
Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic. Traditionally, the existing approaches utilize two independent models without sharing features, which makes the pipeline inefficient to implement. In addition, a heuristic method is usually employed to merge the results. However, the overlapping relationship between object instances is difficult to determine without sufficient context information during the merging process. To address the problems, we propose a novel end-to-end network for panoptic segmentation, which can efficiently and effectively predict both the instance and stuff segmentation in a single network. Moreover, we introduce a novel spatial ranking module to deal with the occlusion problem between the predicted instances. Extensive experiments have been done to validate the performance of our proposed method and promising results have been achieved on the COCO Panoptic benchmark.
1. Introduction
Panoptic segmentation combines pixel-level category labeling with instance separation, but traditional pipelines use separate models and heuristic merging. The paper proposes a shared end-to-end network with learnable fusion and spatial ranking to address efficiency and occlusion.
- 1. Introduction: Panoptic segmentation labels every pixel while segmenting each object instance, combining stuff regions with countable thing objects.The task provides comprehensive scene information for applications including autonomous driving and scene parsing.
- 1. Introduction: Traditional methods train independent instance and stuff models without feature sharing, then merge their predictions through post-processing.This creates computational overhead and makes the pipeline difficult to apply industrially.
- 1. Introduction: The proposed method incorporates both tasks into one network that shares backbone features while using separate task-specific heads.Backbone features receive accumulated supervision from both tasks, whereas each head is fine-tuned for its own task.
- 1. Introduction: A spatial ranking module learns instance ordering to resolve ambiguous overlaps and occlusion relationships during panoptic prediction.The module provides an ordering accordance for predicted instances instead of relying solely on heuristic merging.
- 1. Introduction: The method obtains state-of-the-art performance on the COCO panoptic segmentation dataset.This is reported as the paper’s overall benchmark result.
2. Related Work
Related work spans instance and semantic segmentation, multi-task learning, and early panoptic methods. Existing panoptic systems include joint networks, but the surveyed approaches still include weak supervision, separate task heads with heuristic merging, or broader multi-task frameworks.
- 2. Related Work: Instance segmentation research includes proposal-based methods that detect boxes before predicting masks, with overlap arising from independent instance predictions.These methods are closely connected to object-detection frameworks such as Fast/Faster R-CNN and SPPNet.
- 2. Related Work: Semantic segmentation methods use encoder-decoder recovery, attention, atrous or spatial pyramid pooling, dilated convolution, and multi-scale features to capture object detail and context.The surveyed techniques address resolution recovery, feature selection, localization, and receptive-field size.
- 2. Related Work: The field draws on datasets including VOC, Cityscapes, ADE20K, Mapillary Vistas, and COCO Stuff.These datasets reflect the expansion of resources for semantic and panoptic-related segmentation research.
- 2. Related Work: Panoptic segmentation work includes weakly supervised joint models and single-network systems whose instance and stuff outputs may still be merged heuristically.The cited approaches use weak annotations, instance and pyramid stuff heads, or attention mechanisms.
- 2. Related Work: Multi-task learning frameworks train different visual tasks together and can model relationships among tasks to reduce repetitive learning.Examples include UberNet’s joint vision tasks and Taskonomy’s directed task graph.
3. Proposed End-to-end Framework
The proposed framework unifies stuff and instance segmentation through shared features, then uses a spatial ranking module to resolve instance overlaps during merging. It is trained with joint supervision and derives instance ordering from learned spatial scores.
- 3.1. End-to-end Network Architecture: The stuff branch predicts stuff and object categories from shared feature maps, while the instance branch produces classification, boxes, and masks.The instance branch follows Mask R-CNN with FPN and ROIAlign; the stuff branch uses stacked and concatenated convolutions for multi-scale features.
- 3.1. End-to-end Network Architecture: The total training objective combines five instance losses with a weighted stuff-and-object segmentation loss, while spatial ranking has its own loss.The instance losses cover RPN objectness, RPN boxes, classification, box regression, and mask prediction.
- 3.2. Spatial Ranking Module: The spatial ranking module addresses occlusion by converting instance predictions into an input-sized tensor and learning a ranking score map from non-overlap semantic labels.A large-kernel convolution produces the ranking score map, optimized with pixel-wise cross entropy.
- 3.2. Spatial Ranking Module: Each instance receives an average ranking score over its mask, providing an ordering mechanism for resolving overlapping predictions.The method is motivated by failures of detection-score ordering, such as a person covering a tie, and reports improved performance in that example.
4. Experiments
Experiments evaluate the end-to-end design on COCO panoptic segmentation and ablate loss balance, object context, feature sharing, and spatial ranking. The reported studies identify settings that improve PQ and show the spatial ranking module’s strongest gains on thing categories.
- 4.1. Dataset and Evaluation Metrics: The experiments use COCO panoptic segmentation with 118K training images, 5K validation images, 80 thing categories, and 53 stuff classes, evaluated by PQ.PQ combines segmentation quality for all categories with detection quality for instance classes.
- 4.3. Ablation Study on Network Structure: λ = 0.25 is the optimal loss-balance setting, whereas λ = 1 prevents convergence under the default learning-rate policy.The stuff branch produces dominant gradients, motivating λ to balance instance and stuff supervision.
- 4.3. Ablation Study on Network Structure: 0.5 PQ improvement results when the stuff branch is supervised on all 133 categories instead of only 53 stuff classes.The comparison tests whether object context improves stuff segmentation.
- 4.3. Ablation Study on Network Structure: 0.7% PQ improvement over separate training is obtained by the deep feature-sharing model that shares features through the FPN skip-connection stage.The study compares sharing only backbone features with additionally sharing feature maps before the RPN head.
- 4.4. Ablation Study on Spatial Ranking Module: Replacing instance annotations with non-overlapping panoptic annotations does not improve performance and can reduce object accuracy and recall.The authors attribute this result to most COCO objects not exhibiting the overlapping issue, making forced non-overlap harmful overall.
- 4.4. Ablation Study on Spatial Ranking Module: 1.8% PQ improvement over the end-to-end baseline, including a 2.9% PQTh increase while stuff metrics remain unchanged; the model also achieves state-of-the-art COCO test-dev results.These results are attributed to the spatial ranking module and are evaluated with ResNet-101 on COCO test-dev.
5. Conclusion
The paper presents an end-to-end occlusion-aware panoptic segmentation model that combines semantic and instance segmentation, shares features, and uses spatial ranking. Experiments show that the approach outperforms previous state-of-the-art models.
- The approach outperforms previous state-of-the-art models on panoptic segmentation.
- The model incorporates semantic and instance segmentation into a single end-to-end network.
- Sharing as many features as possible reduces computation-resource consumption across the model branches.
- The spatial ranking module addresses the ranking problem between overlapping object instances.