【论文阅读】semantic image segmentation with deep convolutional nets and fully connected CRFs,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。
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...
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...
• we believe it is advantageous that segmentation is only used at a later stage, avoiding the commitment to premature decisions.他们坚信分割的步骤放在最后的阶段要好一些,以此避免一些前面不成熟的预测。 •The main difference between our model and other state-of-the-art models is the combination...
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)。
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...
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 4.Encoder-Decoder with Atrous Separable Convol...
Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature ...
内容提示: 1DeepLab: Semantic Image Segmentation withDeep Convolutional Nets, Atrous Convolution,and Fully Connected CRFsLiang-Chieh Chen, George Papandreou, Senior Member, IEEE, Iasonas Kokkinos, Member, IEEE,Kevin Murphy, and Alan L. Yuille, Fellow, IEEEAbstract—In this work we address the task...