To tackle the above problem, this paper proposes "Gated CNN" (short for "G-CNN") to introduce a "gate" structure to integrate multiple convolutional layers for object detection. Injected by multi-scale feature layers, a gate employs several filters to extract useful information and block noises...
最后的模型以GatedCNN为一个layer,建立ResNet 文章通过实验提出了一个结论: 序列建模的context size不是越大越好,所以可以抛弃RNN使用CNN建模。 编辑于 2020-09-24 22:11 内容所属专栏 paper-reading 订阅专栏 Paper 卷积神经网络(CNN) RNN 赞同1添加评论 分享喜欢收藏申请转载 ...
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...
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。
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...
A GCNN is a non-recurrent network alternative to capture long-term dependencies while avoiding sequential operations for better parallelizability. Thus, recurrent connections typically applied in RNNs are replaced by gated temporal convolutions. In general, convolutional operations are responsible for hie...
This paper presents a novel MIL approach for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning (TGA-MIL). In contrast to gated attention-based MIL approach, it uses SimCLR for initial CNN parameters instead of being pre-trained fr...
Gated CNN GatedCNN论文LanguageModelingwithGatedConvolutionalNetworks论文ConvolutionalSequence to Sequence Learning Convolutional Sequence to Sequence Learning学习心得 , Jonas,Auli,Michael,Grangier,David, andDauphin,YannN. AConvolutionalEncoder Model for Neural...句子; 且RNN对于句首和句尾的非线性是不一致的2 ...
Noreen Zaffer put forward a CNN-LSTM multi-step prediction model that incorporated feature data with an attention mechanism, showcasing an impressive accuracy rate of nearly 99%, with effective application across varying conditions such as peak and non-peak hours, and differentiating between working ...
WA-CNN [27] 51.5 GTN (CUB200) 56.3 GTN (UCF101) 56.2Table 6: Performance comparisons of classification accuracy (%) with previous work on scene classification, fine-grained recognition, material recognition and action recognition. ∗ indicates our reimplementation result. Our approach achieves stat...