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这几天看了几篇CV语义分割的论文，写了一些围绕着CNN的提升模型性能的插件。在基于预训练微调的大环境下这些插件可以很容易地嵌入模型之中,提高网络提取特征的能力或者信息交互能力（感觉在孪生网络中有很大的发挥空间）。这里写的插件，在很多SOTA模型中会看到它们的影子，甚至就是这些插件的拼接组合。相较于很多基本都在为了play而去plug，结果加入后还不如baseline的Attention，这些插件的效果应该经得起推敲。不过归根结底这些都是trick，只能提升模型的上限，真正有意义的学术还是提升模型的下限。 我是调包侠，用的Keras接口写的，这些插件只要input的shape是（batch_size,height,width,channels）就行，还有一堆超参，调这些超参应该比网络的实现麻烦很多。

module：adaptively_spatial_feature_fusion（ASFF） paper：Adaptively Spatial Feature Fusion Learning Spatial Fusion for Single-Shot Object Detection https://link.zhihu.com/?target=https%3A//arxiv.org/abs/1911.09516v1

module：atrous_spatial_pyramid_pooling（ASPP） paper:DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Conv https://link.zhihu.com/?target=https%3A//arxiv.org/pdf/1606.00915.pdf

module：non_local_block paper:Non-local Neural Networks https://link.zhihu.com/?target=https%3A//arxiv.org/abs/1711.07971

module：squeeze_excite_block (SE) paper:Squeeze-and-Excitation Networks https://link.zhihu.com/?target=https%3A//arxiv.org/pdf/1709.01507.pdf

module：cbam_block paper:CBAM: Convolutional Block Attention Module https://link.zhihu.com/?target=https%3A//openaccess.thecvf.com/content_ECCV_2018/papers/Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.pdf

module：ghost_block paper:GhostNet: More Features from Cheap Operations https://link.zhihu.com/?target=https%3A//arxiv.org/pdf/1911.11907.pdf

module：receptive_field_block paper:Receptive Field Block Net for Accurate and Fast Object Detection https://link.zhihu.com/?target=https%3A//arxiv.org/abs/1711.07767

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