Structural Image Classification with Graph Neural Networks
1. Project Aims 通過將圖像表示爲無向圖來利用區域之間的模式和關係。 使用最近提出的Graph Neural Networks [1]模型來處理結構化數據的分類： 2. Image Structure as Graphs 圖像的結構上下文可以表示爲圖G = {N，E}，其中N（節點）對應於感興趣區域，E（邊緣）對應於兩個不同區域之間的連接。圖1說明了所考慮的結構表示。 Region
相關文章
- 1. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS
- 2. Bag of Tricks for Image Classification with Convolutional Neural Networks
- 3. Applications of Graph Neural Networks
- 4. Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
- 5. Overlapping Community Detection with Graph Neural Networks
- 6. Few-Shot Learning with Graph Neural Networks
- 7. Semi-Supervised Classification With Graph Convolutional Networks
- 8. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
- 9. Semi-Supervised Classification with Graph Convolutional Networks
- 10. ImageNet Classification with Deep Convolutional Neural Networks
- 更多相關文章...
- • RSS
元素 - RSS 教程 - • ASP.NET Image 控件 - ASP.NET 教程
- • Docker 清理命令
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
相關標籤/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
- 1. 升級Gradle後報錯Gradle‘s dependency cache may be corrupt (this sometimes occurs
- 2. Smarter, Not Harder
- 3. mac-2019-react-native 本地環境搭建(xcode-11.1和android studio3.5.2中Genymotion2.12.1 和VirtualBox-5.2.34 )
- 4. 查看文件中關鍵字前後幾行的內容
- 5. XXE萌新進階全攻略
- 6. Installation failed due to: ‘Connection refused: connect‘安卓studio端口占用
- 7. zabbix5.0通過agent監控winserve12
- 8. IT行業UI前景、潛力如何？
- 9. Mac Swig 3.0.12 安裝
- 10. Windows上FreeRDP-WebConnect是一個開源HTML5代理，它提供對使用RDP的任何Windows服務器和工作站的Web訪問
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS
- 2. Bag of Tricks for Image Classification with Convolutional Neural Networks
- 3. Applications of Graph Neural Networks
- 4. Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
- 5. Overlapping Community Detection with Graph Neural Networks
- 6. Few-Shot Learning with Graph Neural Networks
- 7. Semi-Supervised Classification With Graph Convolutional Networks
- 8. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
- 9. Semi-Supervised Classification with Graph Convolutional Networks
- 10. ImageNet Classification with Deep Convolutional Neural Networks