Going Deep
以前球隊在回防時, 教練總會說"快跑! 別回頭, 到定點再休息". 關於人生, 喘息點在哪兒? 我想就在滴水穿石之間! Deep Learning <–> keep learning Week31 (08/04) 網路文章 A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN R-CNN: https://arxiv.org/abs/1311.2524 Visual Recognition就是從這裡爆發了… 透過Selective Search找到Region後丢給修改過的AlexNet學習, 最後再給Support Vector Machine (SVM) 這層CNN. 原本設計的R-CNN就只做這些: 用Selective Search找region 透過pre-trained AlexNet去算特徵後交給SVM看region裡的是什麼鬼東西 最後透過線性回歸(linear regression)畫出物件的座標 Fast R-CNN: https://arxiv.org/abs/1504.08083 Region proposals using Selective Search. RoI (Region of Interest) Pooling Combine All Models into One Network 將R-CNN的AlexNet, SVM, Regressor整合在一起變成單一個network Fast R-CNN instead used a single network to compute the extract image features (CNN), classify (SVM), and tighten bounding boxes (regressor). Faster R-CNN: https://arxiv.org/abs/1506.01497 用SS找region的方法太慢, 又重覆計算, 改由RPN後, 整個framework只要訓練一個CNN. Speeding Up Region Proposal: Selective Search is too slow. A single CNN is used to both carry out region proposals and classification. only one CNN needs to be trained Region Proposal Network(RPN) - How the Regions are Generated ...