Merge pull request #28 from DefTruth/master

Add C++ Demo link

README.md

@@ -217,6 +217,13 @@ Speed is measured with `inference_speed_test.py` for reference.
 * Note 2: GPUs before Turing architecture does not support FP16 inference, so GTX 1080 Ti uses FP32.
 * Note 3: We only measure tensor throughput. The provided video conversion script in this repo is expected to be much slower, because it does not utilize hardware video encoding/decoding and does not have the tensor transfer done on parallel threads. If you are interested in implementing hardware video encoding/decoding in Python, please refer to [PyNvCodec](https://github.com/NVIDIA/VideoProcessingFramework).
 
+<br>  
+
+## Third-party Resources  
+
+* NCNN C++ and Android Demo: [ncnn_Android_RobustVideoMatting](https://github.com/FeiGeChuanShu/ncnn_Android_RobustVideoMatting) from [FeiGeChuanShu](https://github.com/FeiGeChuanShu)
+* ONNXRuntime C++ Demo: [lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit/blob/main/ort/cv/rvm.cpp) and [demo](https://github.com/DefTruth/RobustVideoMatting.lite.ai.toolkit) from [DefTruth](https://github.com/DefTruth)
+
 <br>
 
 ## Project Members

README_zh_Hans.md

@@ -216,7 +216,13 @@ convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter")
 * 注释2：图灵架构之前的 GPU 不支持 FP16 推理，所以 GTX 1080 Ti 使用 FP32。
 * 注释3：我们只测量张量吞吐量（tensor throughput）。 提供的视频转换脚本会慢得多，因为它不使用硬件视频编码/解码，也没有在并行线程上完成张量传输。如果您有兴趣在 Python 中实现硬件视频编码/解码，请参考 [PyNvCodec](https://github.com/NVIDIA/VideoProcessingFramework)。
 
-<br>
+<br>  
+
+## 第三方资源  
+
+* NCNN C++ and Android Demo: [ncnn_Android_RobustVideoMatting](https://github.com/FeiGeChuanShu/ncnn_Android_RobustVideoMatting) from [FeiGeChuanShu](https://github.com/FeiGeChuanShu)
+* ONNXRuntime C++ Demo: [lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit/blob/main/ort/cv/rvm.cpp) and [demo](https://github.com/DefTruth/RobustVideoMatting.lite.ai.toolkit) from [DefTruth](https://github.com/DefTruth)
+
 
 ## 项目成员
 * [Shanchuan Lin](https://www.linkedin.com/in/shanchuanlin/)