作者在论文中argue到,CNN在设计的过程中有一个固有的无效性,因为他们会将color,shape和纹理信息一起处理(感觉可以找个时间介绍一些,图像中的color,shape或者texture信息对于图像的特征提取有哪些帮助作用)。但是实际上这些不同的信息,比如color或者shape,texture对于识别来说的话,应该是包含不同的数量的信息的。作者举...
在常规的CNN骨架网络(Regular Stream)中,并联一个分支结构(Shape Stream),将形状信息作为单独的分支处理(该分支的输入是Regular Stream第一个卷积层的输出,输出是边界信息),在网络最顶层融合两个分支信息,得到最终预测结果。注意:这里对于Regular Stream使用的多类别交叉熵损失函数,而对于Shape Stream用的是binary cross...
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation——论文阅读理解,程序员大本营,技术文章内容聚合第一站。
本篇论文《Gated-SCNN: Gated Shape CNNs for Semantic Segmentation》是作者在NVIDIA工作期间(现在在Google)的一篇将门控卷积和seg结合的paper,文章发表在2019ICCV. 论文地址:openaccess.thecvf.com/c 代码地址(tensorflow):github.com/ben-davidson 代码地址(pytorch):github.com/nv-tlabs/GSC(官方) 数据集:Citys...
论文阅读:Gated-SCNN: Gated Shape CNNs for Semantic Segmentation,程序员大本营,技术文章内容聚合第一站。
具体来讲就是,作者利用传统cnn stream的high-level的information来denoise前几层shape stream的activations。这么一操作,shape stream能够有效的处理相关的信息。而且只使用比较浅的网络。为了能够使得shape information,作者在shape stream上添加了语义边界loss。我们更进一步的利用了一个新的loss function,来使得segmentation ...
Ronneberger et al. proposed the U-shape artificial neural network (U-Net) [14], which was characterized by its encoder–decoder framework. The U-Net has become one of the commonly used methods in medical image segmentations. In order to further improve the segmentation performance, researchers...
Recently, convolutional neural networks (CNNs) have shown great performance for accurate medical image segmentation tasks [5] because of their ability to capture fine details and textures. U-Net [5] adopts a U-shape encoder–decoder architecture to aggregate multi-scale features with skip connection...
Two separate pathways, the top-down and bottom-up pathways, transmit strong semantics from layer at higher level and accurate shape and appearance cues from lower levels. In this paper, we adopt the SSD framework (Fig. 1a) as a meta-architecture to build the gated bidirectional feature ...
作者认为之前的semantic segmentation的工作将所有信息都放入到了CNN的网络之中(这其中包含了颜色,边界等信息),所以作者在regular stream的基础之上增加了一个shape stream的分支,通过利用门控卷积来控制使得shape stream这条分支中只包含边界的信息。 具体的网络结构如下图: 网络一共分三个模块:Regular Stream,Shape St...