Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu,Liang-Chieh Chen,Florian Schroff,Hartwig Adam,Wei Hua,Alan L. Yuille,Li Fei-Fei +6 more
- pp 82-92
TLDR
Li et al. as mentioned in this paper proposed to search the network level structure in addition to the cell level structure, which formed a hierarchical architecture search space and achieved state-of-the-art performance without any ImageNet pretraining.Abstract:
Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this paper, we study NAS for semantic image segmentation. Existing works often focus on searching the repeatable cell structure, while hand-designing the outer network structure that controls the spatial resolution changes. This choice simplifies the search space, but becomes increasingly problematic for dense image prediction which exhibits a lot more network level architectural variations. Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space. We present a network level search space that includes many popular designs, and develop a formulation that allows efficient gradient-based architecture search (3 P100 GPU days on Cityscapes images). We demonstrate the effectiveness of the proposed method on the challenging Cityscapes, PASCAL VOC 2012, and ADE20K datasets. Auto-DeepLab, our architecture searched specifically for semantic image segmentation, attains state-of-the-art performance without any ImageNet pretraining.read more
Citations
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Journal ArticleDOI
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
TL;DR: UNet++ as mentioned in this paper proposes an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision, leading to a highly flexible feature fusion scheme.
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Neural Architecture Search: A Survey
TL;DR: An overview of existing work in this field of research is provided and neural architecture search methods are categorized according to three dimensions: search space, search strategy, and performance estimation strategy.
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Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang,Ke Sun,Tianheng Cheng,Borui Jiang,Chaorui Deng,Yang Zhao,Dong Liu,Yadong Mu,Mingkui Tan,Xinggang Wang,Wenyu Liu,Bin Xiao +11 more
TL;DR: The High-Resolution Network (HRNet) as mentioned in this paper maintains high-resolution representations through the whole process by connecting the high-to-low resolution convolution streams in parallel and repeatedly exchanging the information across resolutions.
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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wenhai Wang,Enze Xie,Xiang Li,Deng-Ping Fan,Kaitao Song,Ding Liang,Tong Lu,Ping Luo,Ling Shao +8 more
TL;DR: Huang et al. as discussed by the authors proposed Pyramid Vision Transformer (PVT), which is a simple backbone network useful for many dense prediction tasks without convolutions, and achieved state-of-the-art performance on the COCO dataset.
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CCNet: Criss-Cross Attention for Semantic Segmentation
TL;DR: This work proposes a Criss-Cross Network (CCNet) for obtaining contextual information in a more effective and efficient way and achieves the mIoU score of 81.4 and 45.22 on Cityscapes test set and ADE20K validation set, respectively, which are the new state-of-the-art results.
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