From 736161cd6cc2a3a87cd84b3ac5d39f8b74d9c102 Mon Sep 17 00:00:00 2001
From: Hussain Kanafani <hussainkanafani@gmail.com>
Date: Tue, 5 Jan 2021 19:44:57 +0100
Subject: [PATCH] Update README.md
---
README.md | 36 +++++++++++++++++++++++++++++++++++-
1 file changed, 35 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index f099551..a6a42e7 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,40 @@ Eventually, it aims to find an unsupervised video summarization method, in order
* [Conda cheat sheet](https://gitlab.uni-hannover.de/hussainkanafani/unsupervised-video-summarization/-/wikis/Conda-cheat-sheet)
+# Datasets
+Structured h5 files with the video features and annotations of the SumMe and TVSum datasets are available within the "data" folder. The GoogleNet features of the video frames were extracted by [Ke Zhang](https://github.com/kezhang-cs) and [Wei-Lun Chao] and the h5 files were obtained from [Kaiyang Zhou](https://github.com/KaiyangZhou/pytorch-vsumm-reinforce).
+
+These files have the following structure:
+```
+/key
+ /features 2D-array with shape (n_steps, feature-dimension)
+ /gtscore 1D-array with shape (n_steps), stores ground truth improtance score (used for training, e.g. regression loss)
+ /user_summary 2D-array with shape (num_users, n_frames), each row is a binary vector (used for test)
+ /change_points 2D-array with shape (num_segments, 2), each row stores indices of a segment
+ /n_frame_per_seg 1D-array with shape (num_segments), indicates number of frames in each segment
+ /n_frames number of frames in original video
+ /picks positions of subsampled frames in original video
+ /n_steps number of subsampled frames
+ /gtsummary 1D-array with shape (n_steps), ground truth summary provided by user (used for training, e.g. maximum likelihood)
+ /video_name (optional) original video name, only available for SumMe dataset
+```
+Original videos and annotations for each dataset are also available in the authors' project webpages:
+
+**TVSum dataset**: [https://github.com/yalesong/tvsum](https://github.com/yalesong/tvsum)
+
+
+**SumMe dataset**: [https://gyglim.github.io/me/vsum/index.html#benchmark](https://gyglim.github.io/me/vsum/index.html#benchmark)
+
+
+
+### CSNet
+We used the implementation of [SUM-GAN](https://github.com/j-min/Adversarial_Video_Summary) method as a starting point to implement CSNet.
+
+#### How to train
+The implementation of CSNet is located under the directory csnet. Run main.py file with the configurations specified in configs.py to train the model.
+
+
+
### SUM-Ind
Make splits
```bash
@@ -45,7 +79,7 @@ python main.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summ
```
-### Citation
+### Citations
```
@article{zhou2017reinforcevsumm,
title={Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward},
--
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