SSD [29] and MSCNN [2] predict objects at multiple layers of the network without merging features. Feature pyramid networks [26] extend the backbone model with a top-down pathway that gradually recovers feature resolution from 1/32 to 1/4, using bilinear upsampling and lateral connection. The...
用于ImageNet的MSDNet具有4个scale,分别在每个图层上生成16、32、64和64个特征图。 network reduction也用于减少计算成本。 在进入MSDNet的第一层之前,首先通过7×7卷积和3×3最大池化(均具有步幅2)对原始图像进行转换。 分类器具有与CIFAR数据集相同的结构,除了每个卷积层的输出通道数设置为等于其输入通道数。 Net...
8.代码 GitHub - proteus1991/GridDehazeNet: This repo contains the official training and testing codes for our paper: GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing.
如图1所示,由CNN接收域(228×228)决定的特征图的单比例尺,可能与小的(如32×32)或大的(如640×640)目标严重不匹配。这降低了目标检测性能。 灵感来自于先前关于图2 (c)的方法优于图2 (b)证据,我们提出一种新的多尺度策略,图2所示(g),这可以被视为深CNN图2 (c)的扩展,但只使用单个输入的规模。它与...
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: ECCV, pp. 184–199 (2014) Dong, C., Loy, C.C., He, K., Tang, X.: Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans. Pattern Anal. Mach. Intel...
the features extracted from the backbone network are first fed into the Layer Feature Fusion Module (LFFM), which integrates the pre- and post-change feature information, thus improving the network's ability to identify the changed regions. The MFPF-Net also contains a Multi-Scale Feature Aggre...
We propose a novel network named Multi-scale Attention-Net with the dual attention mechanism to enhance the ability of feature representation for liver and tumors segmentation 我们提出了一种新的具有双重注意机制的多尺度注意网络,以增强肝脏和肿瘤分割的特征表示能力。
对于人群密度估计问题,由于图像中 scale variations problem,所以提出使用多个CNN来解决 Multi-column/network。使用多个CNN网络导致 网络的参数数量增加,计算量增加,不利于在实际中应用部署。 这里我们采用文献【15】中的 naive Inception module 使用 multi-scale convolutional neural network (MSCNN) 来学习 scale-releva...
To handle the great variability of bi-ventricles, we propose using amulti-scaleconvolutional deep belief network (MCDBN) to learn cardiac image representations (Petersen, Nielsen, Diao, Karssemeijer, Lillholm, 2014, Shin, Orton, Collins, Doran, Leach, 2013). By combining the strengths of both...
均质尺度和非均质尺度的特征都称为omni-scale features;omni可以翻译为“全方位,全面的”。 解决方法: 提出了Omni-Scale Network (OSNet),可以实现omni-scale feature learning;设计了一个residual block,包括多个卷积特征的分支,每一个分支可以检测到某一种尺度的特征;设计了一个aggregation gate用于多种尺度特征的融...