Adversarial Spatio-Temporal Learning for Video Deblurring

时间 2021-01-17

标签去雾论文笔记繁體版

原文原文链接

1. 概述作者将GAN（原始的）应用到视频去雾中，由于2D卷积只能提取输入的位置信息，针对视频连续帧具有时间信息的特点作者采用了3D卷积（部分卷积层中），取得了SOTA的效果。 2. 模型结构生成模型如图1所示，图1 生成模型。表1 生成模型。它是由两个卷积层(L1和L2)， 14个残差块，两个卷积层(L31和L32)没有跳转连接，和三个额外的卷积层数(L33、L34和L35)组成。作者

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1. Spatiotemporal Residual Networks for Video Action Recognition
2. Spatiotemporal CNN for Video Object Segmentation
3. Spatiotemporal Multiplier Networks for Video Action Recognition
4. Collaborative Spatiotemporal Feature Learning for Video Action Recognition 阅读笔记
5. Spatially Adaptive Residual Networks for Efficient Image and Video Deblurring
6. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
7. Adversarial learning for semi-supervised semantic segmentation
8. Adversarial Multi-task Learning for Text Classification
9. 【Active Learning - 05】Adversarial Sampling for Active Learning
10. Research Guide for Video Frame Interpolation with Deep Learning

>>更多相关文章<<