在空洞卷积部分,作者提到使用反卷积的一个弊端是需要额外的内存和计算时间。 上图(上层为1D,下层为2D)很容易可以看出,将空洞卷积的rate 调大,会使得到的feature map更加密集。这里,由于网络最后的卷积池化层分辨率减少很多,因此,在随后的网络中增加rate 为2的空洞卷积,但这里会大大增加计算量,平衡效率与准确率,在...
但是这样计算量就显得有点大,所以他们是采用了一种混合的方法: …but this ends up being too costly. We have adopted instead a hybrid approach that strikes a good efficiency/accuracy trade-off, using atrous convolution to increase by a factor of 4 the density of computed...
4.1 DEEP CONVOLUTIONAL NETWORKS AND THE LOCALIZATION CHALLENGE DCNN score maps can reliably predict the presence and rough position of objects in an image but are less well suited for pin-pointing their exact outline. 得分图可以可靠地预测图像中对象的存在和粗略位置,但不太适合用于刻画精准的轮廓。 De...
DILATED CONVOLUTIONS with kernel size 3x3, dilation=2 局部空间不变性是classifier获得以对象为中心的决策的要求,主要还是由于池化层得作用只保留了局部空间中最重要的信息,作者使用Fully connected CRF(后称DenseCRF)进行全卷积网络训练完成后的后处理,DenseCRF能够在满足长程依赖性的同时捕获细节边缘信息 2. Related...
1.Semantic image segmentation with deep convolutional nets and fully connected CRFs 2.DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs 3.Rethinking Atrous Convolution for Semantic Image Segmentation ...
论文阅读笔记九:SEMANTIC IMAGE SEGMENTATION WITH DEEP CONVOLUTIONAL NETS AND FULLY CONNECTED CRFS (DeepLabv1)(CVPR2014) 论文链接:https://arxiv.org/abs/1412.7062 摘要 该文将DCNN与概率模型结合进行语义分割,并指出DCNN的最后一层feature map不足以进行准确的语义分割,DCNN具有很强的空间不变性,因此比较擅长...
Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected crfs[J]. arXiv preprint arXiv:1412.7062, 2014. Abstract DCNN的最后一层的响应不足以准确定位物体分割,这是由于DCNN的不变性(invariance)属性,这非常适合高级任务(分类,检测等)。
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution--阅读笔记,程序员大本营,技术文章内容聚合第一站。
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution,and Fully CRFs,程序员大本营,技术文章内容聚合第一站。
(alsocalled”semanticimagesegmentation”).WeshowthatresponsesatthefinallayerofDCNNsarenotsufficientlylocalizedforaccurateobjectsegmentation.ThisisduetotheveryinvariancepropertiesthatmakeDCNNsgoodforhighleveltasks.WeovercomethispoorlocalizationpropertyofdeepnetworksbycombiningtheresponsesatthefinalDCNNlayerwitha...