EfficientFCN: Holistically-guided Decoding for Semantic Segmentation

• URL: http://arxiv.org/abs/2008.10487v2
• Date: Thu, 27 Aug 2020 02:42:38 GMT
• Title: EfficientFCN: Holistically-guided Decoding for Semantic Segmentation
• Authors: Jianbo Liu, Junjun He, Jiawei Zhang, Jimmy S. Ren, Hongsheng Li
• Abstract summary: State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN) We propose the EfficientFCN, whose backbone is a common ImageNet pre-trained network without any dilated convolution. Such a framework achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost.
• Score: 49.27021844132522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance. However, due to many convolution operations are conducted on the high-resolution feature maps, such dilatedFCN-based methods result in large computational complexity and memory consumption. To balance the performance and efficiency, there also exist encoder-decoder structures that gradually recover the spatial information by combining multi-level feature maps from the encoder. However, the performances of existing encoder-decoder methods are far from comparable with the dilatedFCN-based methods. In this paper, we propose the EfficientFCN, whose backbone is a common ImageNet pre-trained network without any dilated convolution. A holistically-guided decoder is introduced to obtain the high-resolution semantic-rich feature maps via the multi-scale features from the encoder. The decoding task is converted to novel codebook generation and codeword assembly task, which takes advantages of the high-level and low-level features from the encoder. Such a framework achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost. Extensive experiments on PASCAL Context, PASCAL VOC, ADE20K validate the effectiveness of the proposed EfficientFCN.

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