This is the Mask R-CNN paper. It extends Faster R-CNN to produce pixel masks, i.e., for semantic segmentation. It extends Faster R-CNN by adding a branch for predicting segmentation masks on each Region of Interest (RoI). However, this have to support with RoIAlign instead of RoIPooling, to avoid error introduced...
[more]
Shelhamer, Long, and Darrell (2017) Fully Convolutional Networks for Semantic Segmentation
This journal paper and its 2015 conference paper is to propose a solution for semantic segmentation, i.e., classification of pixels on an image to objects. It is more refined than a bounding box. The paper proposed a solution that involves only convolutional layers but not fully connected layers so that...
[more]
WireGuard VPN
WireGuard is regarded as the only right way to do VPN. There are quite many
benefit of using that. While the configuration is easy, you need to understand
what it is doing to properly set it up.
[more]
Ren, He, Girshick, Sun (2016) Faster R-CNN
This is the Faster R-CNN paper. It improves over Fast R-CNN and R-CNN in the sense that region proposals are generated from the image. It combines a region proposal network (RPN) with the Fast R-CNN detection network so the convolutional network part is shared. It eliminated the use of region...
[more]
Girshick (2015) Fast R-CNN
This is the Fast R-CNN paper. It improves over R-CNN. It is a single-stage
training model (i.e., only one model to train) on the Pascal VOC 2012 dataset.
It is faster than R-CNN by using the RoI pooling layer.
[more]