Conference paper
| Autor: | Qizhu Li, Philip H. S. Torr, Xiaojuan Qi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Pixel business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) 05 social sciences Feature extraction Computer Science - Computer Vision and Pattern Recognition Inference Image segmentation 010501 environmental sciences Object (computer science) 01 natural sciences Pipeline (software) Object detection 0502 economics and business Segmentation Artificial intelligence 050207 economics business 0105 earth and related environmental sciences |
| Zdroj: | CVPR |
| Popis: | We present an end-to-end network to bridge the gap between training and inference pipeline for panoptic segmentation, a task that seeks to partition an image into semantic regions for "stuff" and object instances for "things". In contrast to recent works, our network exploits a parametrised, yet lightweight panoptic segmentation submodule, powered by an end-to-end learnt dense instance affinity, to capture the probability that any pair of pixels belong to the same instance. This panoptic submodule gives rise to a novel propagation mechanism for panoptic logits and enables the network to output a coherent panoptic segmentation map for both "stuff" and "thing" classes, without any post-processing. Reaping the benefits of end-to-end training, our full system sets new records on the popular street scene dataset, Cityscapes, achieving 61.4 PQ with a ResNet-50 backbone using only the fine annotations. On the challenging COCO dataset, our ResNet-50-based network also delivers state-of-the-art accuracy of 43.4 PQ. Moreover, our network flexibly works with and without object mask cues, performing competitively under both settings, which is of interest for applications with computation budgets. CVPR 2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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