Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation (1911.10194v3)

Abstract: In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed. In particular, Panoptic-DeepLab adopts the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression. As a result, our single Panoptic-DeepLab simultaneously ranks first at all three Cityscapes benchmarks, setting the new state-of-art of 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set. Additionally, equipped with MobileNetV3, Panoptic-DeepLab runs nearly in real-time with a single 1025x2049 image (15.8 frames per second), while achieving a competitive performance on Cityscapes (54.1 PQ% on test set). On Mapillary Vistas test set, our ensemble of six models attains 42.7% PQ, outperforming the challenge winner in 2018 by a healthy margin of 1.5%. Finally, our Panoptic-DeepLab also performs on par with several top-down approaches on the challenging COCO dataset. For the first time, we demonstrate a bottom-up approach could deliver state-of-the-art results on panoptic segmentation.

• The paper introduces Panoptic-DeepLab as a unified bottom-up model that combines semantic and instance segmentation using a dual-ASPP and dual-decoder architecture.
• It achieves impressive results with 65.5% PQ on Cityscapes and outperforms previous benchmarks on Mapillary Vistas and COCO datasets.
• Its efficient, parallel design enables faster inference, making it suitable for real-time applications like autonomous driving and video surveillance.

Overview of Panoptic-DeepLab

The paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" presents a novel approach to panoptic segmentation combining semantic and instance segmentation within a single framework. The authors introduce Panoptic-DeepLab, which leverages a dual-ASPP and dual-decoder architecture tailored for bottom-up segmentation methods. This design choice allows it to achieve performance comparable to top-down methods while maintaining faster inference speeds.

Key Contributions

The primary contribution of this work is the development of a unified model that performs both semantic and instance segmentation in a parallel, bottom-up manner. Key architectural elements include:

• Dual-ASPP and Dual-Decoder Architecture: The dual-ASPP (Atrous Spatial Pyramid Pooling) and separate decoder modules for semantic and instance tasks help tailor the context and decoding processes to their respective needs.
• Instance Center Regression: A class-agnostic instance segmentation branch is utilized, where instance centers are predicted through a regression approach, enabling simpler and faster grouping operations.

Numerical Performance

The paper reports compelling results across several datasets:

• On the Cityscapes test set, Panoptic-DeepLab achieves state-of-the-art performance with 65.5% PQ, 39.0% AP, and 84.2% mIoU.
• On the Mapillary Vistas test set, an ensemble approach yields 42.7% PQ, outperforming the 2018 challenge winner by 1.5%.
• On the COCO dataset, Panoptic-DeepLab matches the performance of leading top-down methods in panoptic segmentation, showcasing the effectiveness of the bottom-up strategy.

Theoretical and Practical Implications

The Panoptic-DeepLab demonstrates that bottom-up methods, often sidelined in favor of proposal-based top-down methods, can achieve state-of-the-art results in panoptic segmentation. This shift could encourage further research into bottom-up approaches, which inherently have the potential for faster inference due to their parallel nature.

Practically, these advancements imply that real-time applications in domains such as autonomous driving or video surveillance can leverage such models for efficient and accurate segmentation without the overhead of complex post-processing steps inherent in top-down approaches.

Future Directions

The results presented in this paper open up several avenues for future exploration:

• Enhanced Contextual Understanding: Further exploration into enhancing contextual information and feature fusion within dual-ASPP and dual-decoder architectures could improve segmentation quality.
• Handling Scale Variations: Integrating mechanisms to address large scale variations in images, possibly via learned hierarchical features or multi-scale feature pyramids, could further boost performance.
• Cross-Dataset Generalization: Investigating the adaptability and generalization of these bottom-up methods across diverse datasets beyond those traditionally used in the field could provide broader applicability.

In summary, Panoptic-DeepLab stands as a robust baseline for bottom-up panoptic segmentation, providing a foundation upon which future models can be developed and optimized for both efficiency and performance. The work reinforces the viability of bottom-up approaches in achieving competitive results, posing an intriguing alternative to traditional methods in the domain of image segmentation.