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- W3035295311 abstract "Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution. Current state-of-the-art approaches cannot run in real-time, and simplifying these architectures to improve efficiency severely degrades their accuracy. In this paper, we propose a new single-shot panoptic segmentation network that leverages dense detections and a global self-attention mechanism to operate in real-time with performance approaching the state of the art. We introduce a novel parameter-free mask construction method that substantially reduces computational complexity by efficiently reusing information from the object detection and semantic segmentation sub-tasks. The resulting network has a simple data flow that requires no feature map re-sampling, enabling significant hardware acceleration. Our experiments on the Cityscapes and COCO benchmarks show that our network works at 30 FPS on 1024x2048 resolution, trading a 3% relative performance degradation from the current state of the art for up to 440% faster inference." @default.
- W3035295311 created "2020-06-19" @default.
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- W3035295311 date "2020-06-01" @default.
- W3035295311 modified "2023-10-01" @default.
- W3035295311 title "Real-Time Panoptic Segmentation From Dense Detections" @default.
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- W3035295311 doi "https://doi.org/10.1109/cvpr42600.2020.00855" @default.
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