Seamless Scene Segmentation (1905.01220v1)

Abstract: In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models. The proposed architecture takes advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a light-weight DeepLab-like module. As additional contribution we review the panoptic metric and propose an alternative that overcomes its limitations when evaluating non-instance categories. Our proposed network architecture yields state-of-the-art results on three challenging street-level datasets, i.e. Cityscapes, Indian Driving Dataset and Mapillary Vistas.

Seamless Scene Segmentation

The paper presents a comprehensive paper on seamless scene segmentation by proposing a novel Convolutional Neural Network (CNN) architecture. The objective is to unify the efforts of semantic segmentation and instance segmentation to generate consistent panoptic outputs, surpassing the traditional methodology of combining independently trained segmentation and detection models. This endeavor is aligned with the increasing complexities in automated perception tasks, crucial for applications like autonomous driving and augmented reality.

Architectural Innovation

The architecture leverages a single network backbone to conduct simultaneous semantic and instance segmentation. This unified approach entails a streamlined integration of components, utilizing a novel segmentation head that assimilates multi-scale features from a Feature Pyramid Network (FPN) with contextual information from a lean DeepLab-like module. The proposed architecture results in computational efficiency due to the elimination of redundant information modeling prevalent in separately trained models.

Contributions and Evaluation

The authors highlight several contributions:

• Integration: A cohesive architectural design deploying a single network backbone for both stuff and thing classes segmentation tasks, eliminating the need for independent model training.
• Segmentation Head: Introduction of an innovative segmentation head that fuses FPN's multi-scale capabilities with a lightweight DeepLab-inspired module for contextual enhancement.
• Metric Re-evaluation: The paper revisits the existing panoptic metric, proposing an improvement for evaluating stuff categories more effectively.
• Performance: Experimental evaluation on datasets like Cityscapes, Indian Driving Dataset, and Mapillary Vistas shows that the proposed network delivers state-of-the-art results with significant computational gains.

Quantitatively, the novel architecture demonstrates improvement in panoptic metrics over traditionally fused models, with a haLLMark in computational efficiency. The proposed model achieves PQs upwards of 60% on Cityscapes, illustrating its capability to seamlessly integrate semantic and instance segmentation.

Theoretical and Practical Implications

Theoretically, the research underscores the symbiotic relationship between semantic and instance segmentation tasks, advocating a paradigm shift towards joint model architectures. Practically, it provides a substantial reduction in resource consumption, making it a viable solution for real-time applications like autonomous driving.

Future Directions

Future work may focus on extending this seamless integration to other domains and expanding the versatility of such neural architectures. Additionally, the refinement of panoptic metrics for diverse categories could further align evaluation standards with practical deployment scenarios.

In conclusion, the paper offers a meticulous approach to enhancing scene segmentation, advocating for an integrated model framework. This work not only boosts segmentation efficiencies but also sets a precedent for future explorations into unified models, potentially sparking advancements in the application and development of AI technologies across various fields.