Update README.md

README.md

@@ -8,7 +8,7 @@ This repository is written by Zhaowei Cai at UC San Diego.
 
 This repository implements mulitple popular object detection algorithms, including Faster R-CNN, R-FCN, FPN, and our recently proposed Cascade R-CNN, on the MS-COCO and PASCAL VOC datasets. Multiple choices are available for backbone network, including AlexNet, VGG-Net and ResNet. It is written in C++ and powered by [Caffe](https://github.com/BVLC/caffe) deep learning toolbox. 
 
-[Cascade R-CNN](https://arxiv.org/abs/1712.00726) is a multi-stage extension of the popular two-stage R-CNN object detection framework. The goal is to obtain high quality object detection, which can effectively reject close false positives. It consists of a sequence of detectors trained end-to-end with increasing IoU thresholds, to be sequentially more selective against close false positives. The output of a previous stage detector is forwarded to a later stage detector, and the detection results will be improved stage by stage. This idea can be applied to any detector based on the two-stage R-CNN framework, including Faster R-CNN, R-FCN, FPN, Mask R-CNN, etc, and reliable gains are available independently of baseline strength. A vanilla Cascade R-CNN on FPN detector of ResNet-101 backbone network, without any training or inference bells and whistles, achieved state-of-the-art results on the challenging MS-COCO dataset.
+[Cascade R-CNN](http://www.svcl.ucsd.edu/publications/conference/2018/cvpr/cascade-rcnn.pdf) is a multi-stage extension of the popular two-stage R-CNN object detection framework. The goal is to obtain high quality object detection, which can effectively reject close false positives. It consists of a sequence of detectors trained end-to-end with increasing IoU thresholds, to be sequentially more selective against close false positives. The output of a previous stage detector is forwarded to a later stage detector, and the detection results will be improved stage by stage. This idea can be applied to any detector based on the two-stage R-CNN framework, including Faster R-CNN, R-FCN, FPN, Mask R-CNN, etc, and reliable gains are available independently of baseline strength. A vanilla Cascade R-CNN on FPN detector of ResNet-101 backbone network, without any training or inference bells and whistles, achieved state-of-the-art results on the challenging MS-COCO dataset.
 
 ## Citation