SDDS-Net: Space and Depth Encoder-Decoder Convolutional Neural Networks for Real-Time Semantic Segmentation
Hatem Ibrahem,
Ahmed Salem,
Hyun-Soo Kang
Abstract:In this paper, we propose novel convolutional encoder-decoder architectures for real-time semantic segmentation based on an image-to-image translation approach via the space-to-depth and depth-tospace modules. We present architectures that compress the spatial information of the image using the spaceto-depth (SD) instead of the commonly used pooling methods (Max-pooling and Average-pooling) or strided convolution approaches. The SD module can reduce the image size while preserving the spatial information of th… Show more
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