diff --git a/fluid/PaddleCV/video/README.md b/fluid/PaddleCV/video/README.md
index bf0f27c5ab2955a2437c5195d6ced419559fe6f2..b6b6cdd2dd817268b2fe42f79da8e9e952f96f74 100644
--- a/fluid/PaddleCV/video/README.md
+++ b/fluid/PaddleCV/video/README.md
@@ -6,11 +6,17 @@
 
 | 模型 | 类别  | 描述 |
 | :--------------- | :--------: | :------------: |
-| [Attention Cluster](./models/attention_cluster/README.md) [[论文](https://arxiv.org/abs/1711.09550)] | 视频分类| CVPR'18提出的视频多模态特征注意力聚簇融合方法 |
-| [Attention LSTM](./models/attention_lstm/README.md) [[论文](https://arxiv.org/abs/1503.08909)] | 视频分类| 常用模型，速度快精度高 |
-| [NeXtVLAD](./models/nextvlad/README.md) [[论文](https://arxiv.org/abs/1811.05014)] | 视频分类| 2nd-Youtube-8M最优单模型 |
-| [StNet](./models/stnet/README.md) [[论文](https://arxiv.org/abs/1811.01549)] | 视频分类| AAAI'19提出的视频联合时空建模方法 |
-| [TSN](./models/tsn/README.md) [[论文](https://arxiv.org/abs/1608.00859)] | 视频分类| ECCV'16提出的基于2D-CNN经典解决方案 |
+| [Attention Cluster](./models/attention_cluster/README.md) | 视频分类| CVPR'18提出的视频多模态特征注意力聚簇融合方法 |
+| [Attention LSTM](./models/attention_lstm/README.md)  | 视频分类| 常用模型，速度快精度高 |
+| [NeXtVLAD](./models/nextvlad/README.md)  | 视频分类| 2nd-Youtube-8M最优单模型 |
+| [StNet](./models/stnet/README.md)  | 视频分类| AAAI'19提出的视频联合时空建模方法 |
+| [TSN](./models/tsn/README.md) | 视频分类| ECCV'16提出的基于2D-CNN经典解决方案 |
+
+### 主要特点
+
+- 包含视频分类方向的多个主流领先模型，其中Attention LSTM，Attention Cluster和NeXtVLAD是比较流行的特征序列模型，TSN和StNet是两个End-to-End的视频分类模型。Attention LSTM模型速度快精度高，NeXtVLAD是2nd-Youtube-8M比赛中最好的单模型, TSN是基于2D-CNN的经典解决方案。Attention Cluster和StNet是百度自研模型，分别发表于CVPR2018和AAAI2019，是Kinetics600比赛第一名中使用到的模型。
+
+- 提供了适合视频分类任务的通用骨架代码，用户可一键式高效配置模型完成训练和评测。
 
 ## 安装
 
@@ -52,6 +58,47 @@ bash scripts/train/train_stnet.sh
 
 - 请根据`CUDA_VISIBLE_DEVICES`指定卡数修改`config`文件中的`num_gpus`和`batch_size`配置。
 
+## 模型库结构
+
+### 代码结构
+
+```
+configs/
+  stnet.txt
+  tsn.txt
+  ...
+dataset/
+  youtube/
+  kinetics/
+datareader/
+  feature_readeer.py
+  kinetics_reader.py
+  ...
+metrics/
+  kinetics/
+  youtube8m/
+  ...
+models/
+  stnet/
+  tsn/
+  ...
+scripts/
+  train/
+  test/
+train.py
+test.py
+infer.py
+```
+
+- `configs`: 各模型配置文件模板
+- `datareader`: 提供Youtube-8M，Kinetics数据集reader
+- `metrics`: Youtube-8，Kinetics数据集评估脚本
+- `models`: 各模型网络结构构建脚本
+- `scripts`: 各模型快速训练评估脚本
+- `train.py`: 一键式训练脚本，可通过指定模型名，配置文件等一键式启动训练
+- `test.py`: 一键式评估脚本，可通过指定模型名，配置文件，模型权重等一键式启动评估
+- `infer.py`: 一键式推断脚本，可通过指定模型名，配置文件，模型权重，待推断文件列表等一键式启动推断
+
 ## Model Zoo
 
 - 基于Youtube-8M数据集模型：
@@ -69,6 +116,14 @@ bash scripts/train/train_stnet.sh
 | StNet | 128 | 8卡P40 | 5.1 | 0.69 | [model](https://paddlemodels.bj.bcebos.com/video_classification/stnet_kinetics.tar.gz) |
 | TSN | 256 | 8卡P40 | 7.1 | 0.67 | [model](https://paddlemodels.bj.bcebos.com/video_classification/tsn_kinetics.tar.gz) |
 
+## 参考文献
+
+- [Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification](https://arxiv.org/abs/1711.09550), Xiang Long, Chuang Gan, Gerard de Melo, Jiajun Wu, Xiao Liu, Shilei Wen
+- [Beyond Short Snippets: Deep Networks for Video Classification](https://arxiv.org/abs/1503.08909) Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici
+- [NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification](https://arxiv.org/abs/1811.05014), Rongcheng Lin, Jing Xiao, Jianping Fan
+- [StNet:Local and Global Spatial-Temporal Modeling for Human Action Recognition](https://arxiv.org/abs/1811.01549), Dongliang He, Zhichao Zhou, Chuang Gan, Fu Li, Xiao Liu, Yandong Li, Limin Wang, Shilei Wen
+- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://arxiv.org/abs/1608.00859), Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc Van Gool
+
 ## 版本更新
 
 - 3/2019: 新增模型库，发布Attention Cluster，Attention LSTM，NeXtVLAD，StNet，TSN五个视频分类模型。
diff --git a/fluid/PaddleCV/video/models/stnet/README.md b/fluid/PaddleCV/video/models/stnet/README.md
index f49dfd9c50cfbb60dfc0dcd3631519ca1d40f1b5..2b849ae7ce68f218309b028204269940d702043d 100644
--- a/fluid/PaddleCV/video/models/stnet/README.md
+++ b/fluid/PaddleCV/video/models/stnet/README.md
@@ -105,5 +105,5 @@ StNet的训练数据采用由DeepMind公布的Kinetics-400动作识别数据集
 
 ## 参考论文
 
-[StNet:Local and Global Spatial-Temporal Modeling for Human Action Recognition](https://arxiv.org/abs/1811.01549), Dongliang He, Zhichao Zhou, Chuang Gan, Fu Li, Xiao Liu, Yandong Li, Limin Wang, Shilei Wen
+- [StNet:Local and Global Spatial-Temporal Modeling for Human Action Recognition](https://arxiv.org/abs/1811.01549), Dongliang He, Zhichao Zhou, Chuang Gan, Fu Li, Xiao Liu, Yandong Li, Limin Wang, Shilei Wen
 
diff --git a/fluid/PaddleCV/video/models/tsn/README.md b/fluid/PaddleCV/video/models/tsn/README.md
index a21e048805437f74be3c16c7b9a31d75cd347b58..6b030d9b7008fa320347a75cb2d897df48c50a83 100644
--- a/fluid/PaddleCV/video/models/tsn/README.md
+++ b/fluid/PaddleCV/video/models/tsn/README.md
@@ -81,5 +81,5 @@ TSN的训练数据采用由DeepMind公布的Kinetics-400动作识别数据集。
 
 ## 参考论文
 
-- [StNet:Local and Global Spatial-Temporal Modeling for Human Action Recognition](https://arxiv.org/abs/1608.00859), Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc Van Gool
+- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://arxiv.org/abs/1608.00859), Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc Van Gool