{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/panoptic-feature-pyramid-networks","title":"Panoptic Feature Pyramid Networks","arxiv_id":"1901.02446","date":"2019-01-08","proceeding":"CVPR 2019 6","authors":["Alexander Kirillov","Ross Girshick","Kaiming He","Piotr Dollár"],"abstract":"The recently introduced panoptic segmentation task has renewed our\ncommunity's interest in unifying the tasks of instance segmentation (for thing\nclasses) and semantic segmentation (for stuff classes). However, current\nstate-of-the-art methods for this joint task use separate and dissimilar\nnetworks for instance and semantic segmentation, without performing any shared\ncomputation. In this work, we aim to unify these methods at the architectural\nlevel, designing a single network for both tasks. Our approach is to endow Mask\nR-CNN, a popular instance segmentation method, with a semantic segmentation\nbranch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly,\nthis simple baseline not only remains effective for instance segmentation, but\nalso yields a lightweight, top-performing method for semantic segmentation. In\nthis work, we perform a detailed study of this minimally extended version of\nMask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust\nand accurate baseline for both tasks. Given its effectiveness and conceptual\nsimplicity, we hope our method can serve as a strong baseline and aid future\nresearch in panoptic segmentation.","url_abs":"http://arxiv.org/abs/1901.02446v2","url_pdf":"http://arxiv.org/pdf/1901.02446v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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Segmentation"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"thermal-image-segmentation","task_name":"Thermal Image Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"group-normalization","method_name":"Group Normalization"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"kaiming-initialization","method_name":"Kaiming 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