Here we provide the code for the following paper:
Imbalanced Video Summarization via Spatio-Temporal Deep Architecture
- Nov 21, 2016
- Release the first version VSST (branch: master) compatible with caffe toolbox
First, we provide our trained two-stream CNN models on the SumMe dataset,
"Spatial net model (VSST-RGB&MV)"
"Temporal net model (VSST-RGB&MV)"
"Spatial net model (VSST-imbalance)"
"Temporal net model (VSST-imbalance)"
and CLA dataset.
"Spatial net model (VSST-RGB&MV)"
"Temporal net model (VSST-RGB&MV)"
"Spatial net model (VSST-imbalance)"
"Temporal net model (VSST-imbalance)"
Here, a matlab demo code for VSST is provided.
- Step 1: RGB Extraction
You need extract the RGB frames of the video by yourself. - Step 2: MV Extraction
You need download the motion vector code and compile it by yourself. Motion Vector - Step 3: caffe
You need download the public caffe toolbox. Our VSST code is compatatible with caffe toolbox. - Step 4: train two-stream deep Convnet
- Step 5: Extract feature for each video frame through two-stream deep ConvNet
- Step 6: use SVR to predict the summmary score for each frame