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
- 更多相關文章...
- • XSLT
- • PHP curl_version函數 - PHP參考手冊
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
- • 算法總結-股票買賣
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
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