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EfficientPS is currently ranked #1 for panoptic segmentation on standard benchmark datasets such as Cityscapes, KITTI, Mapillary Vistas, and IDD. Additionally, EfficientPS is also ranked #2 on the Cityscapes semantic segmentation benchmark as well as #2 on the Cityscapes instance segmentation benchmark, among the published methods.

In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which ...

Apr 05, 2020 · EfficientPS: Efficient Panoptic Segmentation 5 Apr 2020 · Rohit Mohan , Abhinav Valada · Edit social preview Understanding the scene in which an autonomous robot operates is critical for its competent functioning.

EfficientPS is the first to benchmark on all the four standard urban scene understanding datasets for panoptic segmentation and exceed the state-of-the-art on each of them while simultaneously being the most efficient model. Achieving computationally efficient rich and coherent image segmentation has widespread implications for image ...

11.05.2020 · Rohit Mohan and Abhinav ValadaEfficientPS: Efficient Panoptic SegmentationPaper: https://arxiv.org/abs/2004.02307Demo: https://rl.uni-freiburg.de/research/pa...

Apr 05, 2020 · 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...

26.02.2021 · More importantly, our EfficientPS architecture not only sets the new state-of-the-art on all the four panoptic segmentation benchmarks, but it is also the most computationally efficient by consuming the least amount of parameters and having the fastest inference time compared to previous state-of-the-art methods.

Apr 05, 2020 · 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.

EfficientPS is a state-of-the-art top-down approach for panoptic segmentation, where the goal is to assign semantic labels (e.g., car, road, tree and so on) to ...

Apr 05, 2020 · 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.

In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and ...

EfficientPS is ranked #1 for panoptic segmentation on the widely used Cityscapes benchmark leaderboard, exceeding the prior state-of-the-art model by a large ...

System Requirements

Named after its capabilities (EfficientPS), the new model's architecture is built upon the popular Mask R-CNN and EfficientNet, which have ...

In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which ...

18 rader · 05.04.2020 · In this paper, we introduce the Efficient Panoptic Segmentation …

In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which ...

Feb 26, 2021 · 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.

In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and ...

05.04.2020 · EfficientPS: Efficient Panoptic Segmentation. 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.