引言 随着深度学习的快速发展,传统的卷积神经网络(Convolutional Neural Networks, CNNs)在计算机视觉领域取得了巨大的成功。然而,对于一些涉及到时序和空间信息的任务,如视频分析、动作识别和人体姿态估计等,传统的CNNs存在一定的局限性。为了有效地处理这些时空信息,研究人员提出了一种新型的卷积神经网络模型,即时空卷积...
However, there is a lack of in-depth analysis of key components of neural networks exhibiting a crucial impact on pulse extraction from video. In this paper, we present a network with attention and spatio-temporal convolutional block (ASTNet), exploiting the impact of key factors including ...
(Multiscale two-stream convolutional neural networks are proposed to capture different sizes of objects in feature learning from a coarse spatial resolution image and two pairs of coarse and fine spatial resolution (FR) images at other dates.) 同时,探讨了时间依赖性和时间一致性作为STFMCNN的补充信息...
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks Christopher Choy chrischoy@stanford.edu JunYoung Gwak jgwak@stanford.edu Silvio Savarese ssilvio@stanford.edu Abstract In many robotics and VR/AR applications, 3D-videos are readily-available input sources...
论文笔记 - C3D - Learning Spatiotemporal Features with 3D Convolutional Networks,程序员大本营,技术文章内容聚合第一站。
【论文阅读笔记】Learning Spatiotemporal Features with 3D Convolutional Networks,程序员大本营,技术文章内容聚合第一站。
Spatiotemporal satellite image fusion using deep convolutional neural networks. IEEE J Sel Top Appl Earth Observations Remote Sens, 2018, 11: 821–829 用于时空融合的双流卷积神经网络(StfNet)在基于CNN的超分辨率过程中考虑了图像序列间的时间依赖性和时间一致性[66]。 [66] Liu X, Deng C, Chanussot ...
Keywords:Leaky Integrate-and-Fire model, multi-threshold, spatio-temporal compression, synaptic convolutional block, spiking neural network 1. Introduction 受人类神经元工作模式的启发,脉冲神经网络(SNN)被认为是第三代人工神经网络[1]。随着SNN的发展,已经展示了广泛的应用,包括图像分类[2][3]、视频处理[4]...
We propose a novel spatiotemporal fusion method based on deep convolutional neural networks (CNNs) under the application background of massive remote sensing data. In the training stage, we build two five-layer CNNs to deal with the problems of complicated correspondence and large spatial resolutio...
Convolutional Neural Networks LSTM: Long short-term memory GRU: Gated Recurrent Unit Recurrent Neural Networks DenseNet: Densely connected convolutional network BN: Batch regularization Relu: Rectified linear unit Conv: Convolution operation BS: Base station ...