論文筆記：Learning Region Features for Object Detection
來源：paper, MSRA & PKU, ECCV 2018 一些講解 作者講解 作者認爲數據驅動的方式訥訥夠提高準確性並且帶來更強的適應性，即將原來人工智能系統各個模塊中手工設置的部分改由可學習的參數。本文主要針對特徵提取部分，例如ROI Pooling方法。 區域特徵提取的一般化視角 基於區域的物體檢測方法步驟總結爲：圖像特徵生成、候選區域生成、區域特徵提取、區域識別和重複檢測去除。在區域特
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相關文章
- 1. 論文筆記：Learning Region Features for Object Detection
- 2. Learning Region Features for Object Detection
- 3. 【論文筆記】：Learning Rich Features at High-Speed for Single-Shot Object Detection
- 4. Learning Rich Features at High-Speed for Single-Shot Object Detection 論文筆記
- 5. Richer Convolutional Features for Edge Detection論文筆記
- 6. 論文筆記 Deep Learning for Generic Object Detection: A Survey （一）
- 7. 論文筆記：Relation Networks for Object Detection
- 8. 【論文筆記】：Overlap Sampler for Region-Based Object Detection
- 9. 論文解析——Deep Reinforcement Learning of Region Proposal Networks for Object Detection
- 10. Learning Deep Features for Discriminative Localization論文筆記