Update README.md

Add an instance segmentation section in cv and MASK_RCNN.
Fix a missing info in classification.

Author: taoyudong

README.md

@@ -190,51 +190,71 @@ Research Paper | Datasets  | Metric | Source Code | Year
       <th align="center" width="20%">Source Code</th>
       <th align="center" width="10%">Year</th>
     </tr>
-        <tr>
+    <tr>
       <td><a href='https://arxiv.org/pdf/1710.09829.pdf'> Dynamic Routing Between Capsules </a></td>
-      <td align="left"> <ul><li> MNIST </li></ul></td>
+      <td align="left"> <ul><li> MNIST </li></ul> </td>
       <td align="left"> <ul><li> Test Error: 0.25±0.005 </li></ul> </td>
-      <td align="left">  <ul><li> <a href='https://github.com/gram-ai/capsule-networks'>PyTorch</a> </li><li> <a href='https://github.com/naturomics/CapsNet-Tensorflow'>Tensorflow</a> </li><li> <a href='https://github.com/XifengGuo/CapsNet-Keras'>Keras</a> </li><li>  <a href='https://github.com/soskek/dynamic_routing_between_capsules'>Chainer</a> </li></ul> </td>
+      <td align="left"> <ul><li> <a href='https://github.com/gram-ai/capsule-networks'>PyTorch</a> </li><li> <a href='https://github.com/naturomics/CapsNet-Tensorflow'>Tensorflow</a> </li><li> <a href='https://github.com/XifengGuo/CapsNet-Keras'>Keras</a> </li><li>  <a href='https://github.com/soskek/dynamic_routing_between_capsules'>Chainer</a> </li></ul> </td>
       <td align="left">2017</td>    
     </tr>
-         <tr>
+    <tr>
       <td><a href='https://arxiv.org/pdf/1102.0183.pdf'> High-Performance Neural Networks for Visual Object Classification </a></td>
       <td align="left"> <ul><li> NORB </li></ul></td>
       <td align="left"> <ul><li> Test Error: 2.53 ± 0.40 </li></ul> </td>
       <td align="left"> <ul><li><a href=''>NOT FOUND</a></ul></li> </td>
       <td align="left">2011</td>    
     </tr>
-           <tr>
+    <tr>
       <td><a href='https://openreview.net/pdf?id=S1NHaMW0b'>ShakeDrop regularization </a></td>
-      <td align="left"> <ul><li>  CIFAR-10  </li> <li> CIFAR-100</li></ul></td>
+      <td align="left"> <ul><li> CIFAR-10 </li> <li> CIFAR-100</li></ul></td>
       <td align="left"> <ul><li> Test Error: 2.31% </li> <li> Test Error: 12.19% </li></ul> </td>
-      <td align="left"><ul><li>  <a href=''>NOT FOUND</a> </li></ul> </td>
+      <td align="left"> <ul><li> <a href=''>NOT FOUND</a> </li></ul> </td>
       <td align="left">2017</td>    
     </tr>
-       <tr>
+    <tr>
       <td><a href='https://arxiv.org/pdf/1611.05431.pdf'>Aggregated Residual Transformations for Deep Neural Networks </a></td>
       <td align="left"> <ul><li>  CIFAR-10  </li></ul></td>
       <td align="left"> <ul><li> Test Error: 3.58% </li></ul> </td>
-      <td align="left"><ul><li>  <a href=''>NOT FOUND</a> </li></ul> </td>
+      <td align="left"> <ul><li>  <a href='https://github.com/facebookresearch/ResNeXt'>PyTorch</a> </li></ul> </td>
       <td align="left">2017</td>    
     </tr>
-           <tr>
+    <tr>
       <td><a href='https://arxiv.org/pdf/1710.09829.pdf'> Dynamic Routing Between Capsules </a></td>
       <td align="left"> <ul><li> MultiMNIST </li></ul></td>
       <td align="left"> <ul><li> Test Error: 5% </li></ul> </td>
-      <td align="left">  <ul><li> <a href='https://github.com/gram-ai/capsule-networks'>PyTorch</a> </li><li> <a href='https://github.com/naturomics/CapsNet-Tensorflow'>Tensorflow</a> </li><li> <a href='https://github.com/XifengGuo/CapsNet-Keras'>Keras</a> </li><li>  <a href='https://github.com/soskek/dynamic_routing_between_capsules'>Chainer</a> </li></ul> </td>
+      <td align="left"> <ul><li> <a href='https://github.com/gram-ai/capsule-networks'>PyTorch</a> </li><li> <a href='https://github.com/naturomics/CapsNet-Tensorflow'>Tensorflow</a> </li><li> <a href='https://github.com/XifengGuo/CapsNet-Keras'>Keras</a> </li><li>  <a href='https://github.com/soskek/dynamic_routing_between_capsules'>Chainer</a> </li></ul> </td>
       <td align="left">2017</td>    
     </tr>
-           <tr>
+    <tr>
       <td><a href='https://arxiv.org/pdf/1611.05431.pdf'>Aggregated Residual Transformations for Deep Neural Networks </a></td>
       <td align="left"> <ul><li>   ImageNet-1k  </li></ul></td>
       <td align="left"> <ul><li> Top-1 Error: 20.4% </li></ul> </td>
-      <td align="left"><ul> <li>  <a href='https://github.com/facebookresearch/ResNeXt'>PyTorch</a> </li></ul> </td>
+      <td align="left"> <ul><li>  <a href='https://github.com/facebookresearch/ResNeXt'>PyTorch</a> </li></ul> </td>
       <td align="left">2016</td>    
     </tr>
   </tbody>
 </table>
 
+#### 2. Instance Segmentation
+<table>
+  <tbody>
+    <tr>
+      <th width="30%">Research Paper</th>
+      <th align="center" width="20%">Datasets</th>
+      <th align="center" width="20%">Metric</th>
+      <th align="center" width="20%">Source Code</th>
+      <th align="center" width="10%">Year</th>
+    </tr>
+    <tr>
+      <td><a href='https://arxiv.org/pdf/1703.06870.pdf'>Mask R-CNN</a></td>
+      <td align="left"> <ul><li> COCO  </li></ul></td>
+      <td align="left"> <ul><li> Average Precision: 37.1% </li></ul> </td>
+      <td align="left"> <ul><li>  <a href='https://github.com/matterport/Mask_RCNN'>TensorFlow (This is not official. Performance is worse than the reported AP)</a> </li></ul> </td>
+      <td align="left">2017</td>    
+    </tr>
+  </tbody>
+</table>
+
 ### Speech
 #### 1. ASR
 Research Paper | Datasets  | Metric | Source Code | Year