論文閱讀：Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
FactorizableNet（ECCV2018） 文章 Paper認爲目前有兩種生成scene graph的方法，一種是two-stage的，先把object檢測到（包括類別），然後再recognize他們之間的關係，另一種是基於region proposal對object的類別和他們之間的relation進行聯合推理。兩種方法的共同之處是都會用到object pair的union box的
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相關文章
- 1. Graph R-CNN for Scene Graph Generation
- 2. 論文閱讀：Scene Graph Generation by Iterative Message Passing
- 3. 【論文閱讀·1】EvoGraph: An Effective and Efficient Graph Upscaling Method for Preserving Graph Properties
- 4. 論文閱讀（7）：VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight FR
- 5. 《論文閱讀》SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
- 6. 【論文閱讀】Graphicionado:A High-Performance and Energy-Efficient Accelerator for Graph Analytics
- 7. 【論文閱讀筆記】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 8. 論文閱讀筆記：ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 9. 【論文閱讀】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 10. 論文閱讀：An Efficient Statistical Method for Image Noise Level Estimation