论文阅读：Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation

时间 2021-01-02

标签 scene understand 繁體版

原文原文链接

FactorizableNet（ECCV2018）文章 Paper认为目前有两种生成scene graph的方法，一种是two-stage的，先把object检测到（包括类别），然后再recognize他们之间的关系，另一种是基于region proposal对object的类别和他们之间的relation进行联合推理。两种方法的共同之处是都会用到object pair的union box的

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1. Graph R-CNN for Scene Graph Generation
2. 论文阅读：Scene Graph Generation by Iterative Message Passing
3. 【论文阅读·1】EvoGraph: An Eﬀective and Eﬀicient Graph Upscaling Method for Preserving Graph Properties
4. 论文阅读（7）：VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight FR
5. 《论文阅读》SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
6. 【论文阅读】Graphicionado:A High-Performance and Energy-Efficient Accelerator for Graph Analytics
7. 【论文阅读笔记】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
8. 论文阅读笔记：ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
9. 【论文阅读】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
10. 论文阅读：An Efficient Statistical Method for Image Noise Level Estimation

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