Multi-task Network for Panoptic Segmentation in Automated Driving

Multi-task Network for Panoptic Segmentation in Automated Driving. Petrovai, A. & Nedevschi, S. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Institute of Electrical and Electronics Engineers Inc., 10, 2019.

Paper doi abstract bibtex

In this paper, we tackle the newly introduced panoptic segmentation task. Panoptic segmentation unifies semantic and instance segmentation and leverages the capabilities of these complementary tasks by providing pixel and instance level classification. Current state-of-the-art approaches employ either separate networks for each task or a single network for both task and post processing heuristics fuse the outputs into the final panoptic segmentation. Instead, our approach solves all three tasks including panoptic segmentation with an end-to-end learnable fully convolutional neural network. We build upon the Mask R-CNN framework with a shared backbone and individual network heads for each task. Our semantic segmentation head uses multi-scale information from the Feature Pyramid Network, while the panoptic head learns to fuse the semantic segmentation logits with variable number of instance segmentation logits. Moreover, the panoptic head refines the outputs of the network, improving the semantic segmentation results. Experimental results on the challenging Cityscapes dataset demonstrate that the proposed solution achieves significant improvements for both panoptic segmentation and semantic segmentation.

@article{
 title = {Multi-task Network for Panoptic Segmentation in Automated Driving},
 type = {article},
 year = {2019},
 pages = {2394-2401},
 month = {10},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 day = {1},
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 accessed = {2023-11-08},
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 abstract = {In this paper, we tackle the newly introduced panoptic segmentation task. Panoptic segmentation unifies semantic and instance segmentation and leverages the capabilities of these complementary tasks by providing pixel and instance level classification. Current state-of-the-art approaches employ either separate networks for each task or a single network for both task and post processing heuristics fuse the outputs into the final panoptic segmentation. Instead, our approach solves all three tasks including panoptic segmentation with an end-to-end learnable fully convolutional neural network. We build upon the Mask R-CNN framework with a shared backbone and individual network heads for each task. Our semantic segmentation head uses multi-scale information from the Feature Pyramid Network, while the panoptic head learns to fuse the semantic segmentation logits with variable number of instance segmentation logits. Moreover, the panoptic head refines the outputs of the network, improving the semantic segmentation results. Experimental results on the challenging Cityscapes dataset demonstrate that the proposed solution achieves significant improvements for both panoptic segmentation and semantic segmentation.},
 bibtype = {article},
 author = {Petrovai, Andra and Nedevschi, Sergiu},
 doi = {10.1109/ITSC.2019.8917422},
 journal = {2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019}
}

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