Weakly-Supervised-Dense-Video-Captioning

This repo try to implement Weakly Supervised Dense Video Captioning in tensorflow but not complete yet.

Requirement

• Python 3
• Keras 2.2
• Tensorflow 1.8

Usage

• Run lexical_Res.py for training FCN with MIML loss while saving weights with the lowest loss.
• Run region_selection.py to generate most informative and coherrence region sequence.
• Run TRY3/model_seq2seq.py to train language model.
• While using TRY3/s2vt_predict_v2.py to inference the model.

Guide

1. extract_frames.py: Uniform sampling 30 frames for each video.
2. load_data.py: Create label vector and word dictionary.
3. Res_video_bag.py: Lexical FCN(Resnet50) with a frame as an instance.
4. lexical_Res.py: Lexical FCN(Resnet50) with a region as an instance.
5. region_selection.py: Region sequence generator, which cound form one region sequence now.
6. dic/: Where to put ix2word, word2ix, word_counts.
7. frames/: Where to put frames extracted by extract_frames.py.
8. MSRVTT/: Where to put training/testing labels and region sequences generated by region_selection.py.
9. videos/: Where to put the MSR-VTT videos.
10. Weight_Resnet50/: Where to put weight save from lexical_Res.py.
11. Weight_Resnet50_vasbag/: Where to put weight save from Res_video_bag.py
12. TRY3/s2vt_train.py: Language model using S2VT.(train)
13. TRY3/s2vt.py: S2VT model graph.
14. TRY3/s2vt_inference.py: Language model using S2VT.(inference)

Reference

• Weakly Supervised Dense Video Captioning
• s2vt

Contact

Shih-Chen Lin (dennis60512@gmail.com)

Any discussions and suggestions are welcome!