EfficientPS: Efficient Panoptic Segmentation

By Rohit Mohan, Abhinav Valada. 2020

260 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Efficiency & Compression

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Efficiency & Compression

Citations in the corpus

Novel Class Discovery without Forgetting (2022, background): “Over the last decade, deep learning algorithms have achieved remarkable performances on multiple computer vision tasks [15,37,50,7,45], even outperforming humans on many of them.”
Image Segmentation Using Deep Learning: A Survey (2020, methodology): “Efforts in this direction include Panoptic Feature Pyramid Network (PFPN) [144], attention-guided network for panoptic segmentation [145], seamless scene segmentation [146], panoptic Deeplab [147], unified panoptic segmentation network [148], and efficient panoptic segmentation [149].”
Spacing Loss for Discovering Novel Categories (2022, background): “Availability of large amount of annotated data has fueled unprecedented success of deep learning in various machine learning tasks [5, 9, 18, 30, 36, 37].”