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What Led to Faster R-CNN? Even though networks like Fast R-CNN achieve a really high accuracy, they are very slow to be of practical use in real time. The reason for such slow speeds is the region proposal step in the architecture! Algorithms, like Selective Search (used in Fast R-CNN) take around 2 seconds per … Continue reading Paper Explanation: Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks
Called Fast R-CNN because it's comparatively fast to train and test. Why Fast R-CNN? Even though R-CNN had achieved state of the art performance on object detection it had many problems: Slow at test-time due to independent forward passes of the CNN for each region proposal. The CNN doesn't get trained during the training of the … Continue reading Paper Explanation: Fast R-CNN
To what extent do the CNN classification generalise to object detection? Object detection is the task of finding the different objects in an image and classifying them (as seen in the image above). This paper is the first to show that a CNN can lead to dramatically higher object detection performance Let’s now take a moment … Continue reading Paper Explanation: Rich feature hierarchies for accurate object detection and semantic segmentation (R-CNN)
Mohit Jain 