Learning Attentive Pairwise Interaction for Fine-Grained Classification论文解读
论文链接：https://arxiv.org/abs/2002.10191 分享的这篇文章来自于AAAI2020，文章的整个思路并不难理解。文章的idea来自于我们人类对相似图像的识别。一般来说，我们识别相似的图像，一方面是去找到图像中特殊的区域来进行识别，另一方面是通过比较图像对来有效地得到对比线索。这篇论文就是从后者获得的灵感。 文章提出了Attentive Pairwise Interact
相关文章
- 1. 论文阅读笔记《Generative Adversarial Residual Pairwise Networks for One Shot Learning 》
- 2. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
- 3. 论文阅读笔记《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 4. 论文阅读理解 - Learning Spatial Regularization for Multi-label Image Classification
- 5. CVPR 2017 Deep representation learning for human motion prediction and classification 论文解读
- 6. CVPR2016(classification)：论文解读《Deep Residual Learning for Image Recognition》
- 7. Pairwise Confusion for Fine-Grained Visual Classification
- 8. 论文阅读：Action recognition for human-marionette interaction.
- 9. 论文学习4-An Attentive Neural Architecture for Fine-grained Entity Type Classification
- 10. 论文阅读笔记 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 更多相关文章...
- • C# 文本文件的读写 - C#教程
- • ARP报文格式详解 - TCP/IP教程
- • JDK13 GA发布：5大特性解读
- • Scala 中文乱码解决
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 论文阅读笔记《Generative Adversarial Residual Pairwise Networks for One Shot Learning 》
- 2. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
- 3. 论文阅读笔记《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 4. 论文阅读理解 - Learning Spatial Regularization for Multi-label Image Classification
- 5. CVPR 2017 Deep representation learning for human motion prediction and classification 论文解读
- 6. CVPR2016(classification)：论文解读《Deep Residual Learning for Image Recognition》
- 7. Pairwise Confusion for Fine-Grained Visual Classification
- 8. 论文阅读：Action recognition for human-marionette interaction.
- 9. 论文学习4-An Attentive Neural Architecture for Fine-grained Entity Type Classification
- 10. 论文阅读笔记 | (ECCV 2018) Learning to Navigate for Fine-grained Classification