Enhanced receptive field SPP, ASPP, RFB, PPM
Enhanced receptive field SPP, ASPP, RFB, PPM_yx868xy's blog-CSDN blog
Good article on graph neural network at ICLR 2023
Good articles on graph neural networks at ICLR 2023 - Zhihu (zhihu.com)
More CVPR 2023 papers and open source projects
amusi/CVPR2023-Papers-with-Code: CVPR 2023 papers and open source project collection (github.com)
Huawei Noah proposes VanillaNet: The power of minimalism in deep learning
Selected for CVPR 2023 papers and open source projects
Huawei Noah proposes VanillaNet: The power of minimalism in deep learning - Zhihu (zhihu.com)
See Zhihu for code and documentation
CVPR 2022 | Mobile-Former is here! Microsoft proposes: MobileNet+Transformer lightweight parallel network
CVPR 2022 | Huazhong University of Science and Technology & Tencent open source TopFormer: Transformer for mobile semantic segmentation
PPM Pyramid Pooling Module-PSPNet
Article address and source code: http://t.csdn.cn/ZzDah
GFF: Gated Full Fusion for Semantic Segmentation
Publication time: 2020-02
Paper link: [1904.01803] GFF: Gated Full Fusion for Semantic Segmentation (arxiv.org)
Interpretation of the paper: http://t.csdn.cn/TwTKc
Code address: https://github.com/lxtGH/DecoupleSegNets
DecoupleSegNets
Interpretation of the article: DecoupleSegNets learning summary_decouple model lane lines_Xiao Tian Yao Run's blog-CSDN blog
Code address: https://github.com/lxtGH/DecoupleSegNets
Change the UNet encoding part to MobileNet
Top Issue TIP 2023 | CFP: Plug-and-play multi-scale fusion module helps effectively increase detection and segmentation tasks!
Article Interpretation: Top Issue TIP 2023 | CFP: Plug-and-play multi-scale fusion module helps to effectively increase detection and segmentation tasks! - Know almost
Code address: CFPNet: Centralized Feature Pyramid for Object Detection (github.com)
MobileNet v3
Article interpretation:
The first choice for lightweight skeleton: MobileNetV3 complete analysis - Zhihu (zhihu.com)
Code address: https://github.com/xiaolai-sqlai/mobilenetv3
Network structure of Small version and Large version