Rich feature hierarchies for accurate object detection and semantic segmentation論文細讀
因爲研究方向是目標檢測，所以最近開始讀文獻了，因爲自己知識淺薄，如有不足請見諒，歡迎互相交流。 論文算法特點的簡單介紹： 1.使用了大容量CNN自上而下的提取候選區域以便進行目標檢測和分割。 2.在標記數據較爲匱乏時，使用監督學習在輔助數據集上進行預訓練，然後在針對特定區域進行微調，大幅度提高了性能表現。 RCNN的步驟 1.首先使用Selective Search方法生成2000個region
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相關文章
- 1. Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. 【論文閱讀】Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. 【論文快讀】Rich feature hierarchies for accurate object detection and semantic segmentation
- 4. 【論文閱讀筆記】Rich feature hierarchies for accurate object detection and semantic segmentation
- 5. 論文解讀：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 6. 論文學習《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 7. R-CNN論文翻譯Rich feature hierarchies for accurate object detection and semantic segmentation
- 8. 論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation
- 9. 【論文學習】Rich feature hierarchies for accurate object detection and semantic segmentation
- 10. R-CNN（Rich feature hierarchies for accurate object detection and semantic segmentation）論文理解...