EfficientPS: Efficient Panoptic Segmentation (2004.02307v3)

Abstract: Understanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.

Overview of EfficientPS: Efficient Panoptic Segmentation

The paper presents EfficientPS, a novel architectural solution for the complex task of panoptic segmentation aimed at advancing scene understanding capabilities in autonomous driving and other robotics applications. EfficientPS integrates semantic and instance segmentation into a single framework, enabling holistic scene interpretation crucial for intelligent robotic behavior. It introduces multiple innovations including a shared backbone with an improved feature pyramid network, specialized task-specific heads, and a novel fusion mechanism, achieving state-of-the-art efficiency and performance across multiple benchmarks.

EfficientPS differentiates itself primarily through its architectural design which enhances both efficiency and accuracy in panoptic segmentation. Key to its structure is the use of a modified EfficientNet backbone, facilitating rich feature representation with significantly reduced parameter demand. This is supplemented with a novel 2-way Feature Pyramid Network (FPN) which uniquely allows bidirectional information flow, contrasting the traditional unidirectional schemes.

The semantic head of EfficientPS adeptly balances global context with fine detail. It utilizes dense prediction cells to efficiently capture multiscale contexts and addresses feature misalignment through a carefully designed mechanism. Alongside, it employs a variant of Mask R-CNN as the instance head, optimized with depthwise separable convolutions to preserve parameter efficiency.

A central challenge in panoptic segmentation—the seamless fusion of semantic and instance outputs—is addressed via an adaptive, parameter-free panoptic fusion module. This module adeptly combines logits from distinct segmentations, dynamically modulating their contributions based on contextual agreement. Through intelligent fusion, it sidesteps common pitfalls of overlap reduction methods, enhancing class differentiation without compromising instance integrity.

This work further introduces the KITTI panoptic segmentation dataset, enriching it with annotations complementing its existing perception tasks. This provides researchers with a robust platform for multi-task learning studies, especially concerning autonomous urban navigation.

Strong Numerical Results

In evaluating EfficientPS, the paper reports an exceptional performance across four challenging urban scene benchmarks: Cityscapes, KITTI, Mapillary Vistas, and the Indian Driving Dataset (IDD). Amongst the notable performances, EfficientPS achieves a PQ score of 66.4% on the Cityscapes dataset when trained on fine annotations alone, outstripping various preceding methodologies. Moreover, it showcases superior computational efficiency, boasting the fastest inference time, and the least number of parameters among contemporary state-of-the-art models.

Theoretical and Practical Implications

EfficientPS not only sets new performance benchmarks but also brings computational efficiency to the forefront of panoptic segmentation. The introduction of an architecture which marries high efficiency with cutting-edge accuracy exemplifies a pragmatic approach well-suited for real-time applications like autonomous driving. The leveraging of modified EfficientNet models and the innovative panoptic fusion module offer pathways for reduced model complexity without sacrificing representational power, suggesting potential applications in resource-constrained environments.

Future Directions

The authors propose further leveraging of the KITTI panoptic segmentation dataset for developing multi-task learning frameworks. Additionally, exploring hybrid models that blend the predictive strengths of both top-down and bottom-up pathways might provide further gains in segmentation performance. Investigating more sophisticated fusion schemes to better integrate logits from varied sources can be another promising avenue, potentially improving the fusion strategy applied in EfficientPS.

EfficientPS represents a substantial step forward, reflecting how efficient architectures can achieve state-of-the-art results while maintaining lower computational overhead. Its approach to unifying semantic and instance segmentation tasks underlines the potential for continued innovation in areas demanding real-time, comprehensive scene understanding. This research lays groundwork poised to impact a broad range of applications within the field of robotics and beyond.