R-CNN(Regions with CNN features)
本篇論文是目標檢測的開山之作，之後的大部分文章都是基於本篇論文進行修改和優化得到的產物。 之前的目標檢測算法是窮舉搜索（ExhaustiveSearch）：使用一個窗口在圖片上進行滑動，改變窗口的大小，繼續掃描整張圖像。通過尋找響應值最高的那個位置作爲預測的目標。而本文是首先從圖像中提取可能是物體的區域，然後對這些區域進行判斷。這樣的做的優點是過濾掉了一些無用的box ,節省了時間。 主要流程
相關文章
- 1. Learning Transferable Features with Deep Adaptation Networks
- 2. Language Identification with Deep Bottleneck Features
- 3. Faster R-CNN Features for Instance Search
- 4. Content-based image retrieval with compact deep convolutional features
- 5. 翻譯「Learning Transferable Features with Deep Adaptation Networks」
- 6. zoom-out: Feedforward semantic segmentation with zoom-out features
- 7. 2D Features framework7(Feature Matching with FLANN )
- 8. Regions with Convolutional NeuralNetwork Features (RCNN) 學習筆記
- 9. Visual saliency detection based on multiscale deep cnn features
- 10. [zoom-out]Feedforward semantic segmentation with zoom-out features
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相關文章
- 1. Learning Transferable Features with Deep Adaptation Networks
- 2. Language Identification with Deep Bottleneck Features
- 3. Faster R-CNN Features for Instance Search
- 4. Content-based image retrieval with compact deep convolutional features
- 5. 翻譯「Learning Transferable Features with Deep Adaptation Networks」
- 6. zoom-out: Feedforward semantic segmentation with zoom-out features
- 7. 2D Features framework7(Feature Matching with FLANN )
- 8. Regions with Convolutional NeuralNetwork Features (RCNN) 學習筆記
- 9. Visual saliency detection based on multiscale deep cnn features
- 10. [zoom-out]Feedforward semantic segmentation with zoom-out features