.png){kind=icon}
返回
Dense Convolutional Networks for Semantic Segmentation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2908685.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent studies have greatly promoted the development of semantic segmentation. Most state-of-the-art methods adopt fully convolutional networks (FCNs) to accomplish this task, in which the fully connected layer is replaced with the convolution layer for dense prediction. However, standard convolution has limited ability in maintaining continuity between predicted labels as well as forcing local smooth. In this paper, we propose the dense convolution unit (DCU), which is more suitable for pixel-wise classiffication. The DCU adopts dense prediction instead of the center-prediction manner used in current convolution layers. The semantic label for every pixel is inferred from those overlapped center/off-center predictions from the perspective of probability. It helps to aggregate contexts and embeds connections between predictions, thus successfully generating accurate segmentation maps. DCU serves as the classiffication layer and is a better option than standard convolution in FCNs. This technique is applicable and beneficial to FCN-based state-of-the-art methods and works well in generating segmentation results. Ablation experiments on benchmark datasets validate the effectiveness and generalization ability of the proposed approach in semantic segmentation tasks.
Keyword:
Dense convolution unit
fully convolutional network
overlapped prediction
semantic segmentation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition更广泛或更深入: 重新审视视觉识别的ResNet模型.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏Electrochemically assisted micro localized grafting of aptamers in a microchannel engraved in fluorinated thermoplastic polymer Dyneon THVRSC Advances
RSC AdvancesIF0
PREAI
分享收藏Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic SegmentationIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容