Learning Rich Features at High-Speed for Single-Shot Object Detection
Learning Rich Features at High-Speed for Single-Shot Object Detection abstract 單級目標檢測方法因其具有實時性強、檢測精度高等特點，近年來受到廣泛關注。通常，大多數現有的單級檢測器遵循兩個常見的實踐:它們使用在ImageNet上預先訓練的網絡主幹來完成分類任務，並使用自頂向下的特徵金字塔表示來處理規模變化。作者研究了
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相關文章
- 1. 《Learning Rich Features at High-Speed for Single-Shot Object Detection》筆記
- 2. 【論文筆記】：Learning Rich Features at High-Speed for Single-Shot Object Detection
- 3. Learning Rich Features at High-Speed for Single-Shot Object Detection 論文筆記
- 4. Learning Region Features for Object Detection
- 5. Learning Rich Features from RGB-D Images for Object Detection and Segmentation(ECCV2014)
- 6. 論文筆記：Learning Region Features for Object Detection
- 7. Learning Rich Features for Image Manipulation Detection(CVPR 2018 圖像篡改檢測)
- 8. Rich feature hierarchies for accurate object detection and semantic segmentation
- 9. Rapid Object Detection using a Boosted Cascade of Simple Features
- 10. RILOD Near Real-Time Incremental Learning for Object Detection at the Edge 翻譯