In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3×3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

A Robust Image Zero-watermarking Using Convolutional Neural Networks

Aggregating Deep Convolutional Features for Image Retrieval.pdf Aggregating Deep Convolutional Features for Image Retrieval.pdf Aggregating Deep Convolutional Features for Image Retrieval.pdf

十分好的文章。

图卷积网络 GCN Graph Convolutional Network（谱域GCN）的理解和详细推导博客pdf 原博客链接：https://blog.csdn.net/yyl424525/article/details/100058264#comments_12499724

人工智能领域—计算机视觉最新文章观察，2019《A Survey of the Recent Architectures of Deep Convolutional Neural Networks》67页pdf原文

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A shortly and densely connected convolutional neural network for vehicle re-identification

Correlative Filters for Convolutional Neural Networks

CVPR2020:组合卷积神经网络:对部分遮挡物具有天生鲁棒性的深度架构_论文阅读