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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. We address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit.

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刚刚,谷歌开源了语义图像分割模型 DeepLab-v3+,DeepLab-v3+结合了空间金字塔池化模块和编码器-解码器结构的优势,是自三年前的 DeepLab 以来的最新、性能最优的版本。

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The key difference between this version 4 and BSS Eval version 3 is the possibility of using the same distortion filters for all windows when matching the sources to their estimates, instead of estimating the filters anew at every frame, as done in BSS Eval v3. This implementation is fully compatible with BSS Eval v2 and v3 written in MATLAB.

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DeepLab V3+ 논문의 경우 뛰어난 novelty가 존재하는 것은 아니지만, DeepLab V1부터 시작해 꾸준히 semantic segmentation 성능을 향상시키기 위한 방법론을 연구하는 단계의 최신선상에 놓인 논문이며, encoder, ASPP, decoder 각 모듈이 수행하는 역할이 명확하고 모듈화 되어 있어 ... Nov 22, 2019 · In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed. In particular, Panoptic-DeepLab adopts the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation ...

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