Efficient pyramid context encoding and feature embedding for semantic segmentation
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摘要
For reality applications of semantic segmentation, inference speed and memory usage are two important factors. To address these challenges, we propose a lightweight feature pyramid encoding network (FPENet) for semantic segmentation with a good trade-off between accuracy and speed. We use a series of feature pyramid encoding (FPE) blocks to encode context at multiple scales in the encoder. Each FPE block consists of different depthwise dilated convolutions that perform as a spatial pyramid to extract features and reduce computational costs. During training, a one-shot neural architecture search algorithm is adopted to find the optimal structure for each FPE block from a large search space with a small search cost. After the search for the encoder, a mutual embedding upsample module is introduced in the decoder, consisting of two attention blocks. The encoder-decoder attention mechanism is used to help aggregate efficiently high-level semantic features and low-level spatial details. The proposed network outperforms the existing real-time methods with fewer parameters and improved inference speed on the Cityscapes and CamVid benchmark datasets. Specifically, it achieved 72.3% mean IoU on the Cityscapes test set with only 0.4 M parameters and 192.6 FPS speed on an Nvidia Titan V100 GPU, and 73.4% mean IoU with 116.2 FPS when running on higher resolution images.
论文关键词:Semantic segmentation,Convolutional neural networks,Pyramid context encoding,Real-time processing
论文评审过程:Received 29 April 2021, Accepted 1 May 2021, Available online 8 May 2021, Version of Record 17 May 2021.
论文官网地址:https://doi.org/10.1016/j.imavis.2021.104195