Skip to content

Latest commit

 

History

History
executable file
·
103 lines (71 loc) · 3.44 KB

README.md

File metadata and controls

executable file
·
103 lines (71 loc) · 3.44 KB
Raw

Consensus-based Sequence Training for Video Captioning

Code for the video captioning methods from "Consensus-based Sequence Training for Video Captioning" (Phan, Henter, Miyao, Satoh. 2017).

Dependencies

(Check out the coco-caption and cider projects into your working directory)

Data

Data can be downloaded here (643 MB). This folder contains:

  • input/msrvtt: annotatated captions (note that val_videodatainfo.json is a symbolic link to train_videodatainfo.json)
  • output/feature: extracted features
  • output/model/cst_best: model file and generated captions on test videos of our best run (CIDEr 54.2)

Getting started

Extract video features

  • Extracted features of ResNet, C3D, MFCC and Category embeddings are shared in the above link

Generate metadata

make pre_process

Pre-compute document frequency for CIDEr computation

make compute_ciderdf

Pre-compute evaluation scores (BLEU_4, CIDEr, METEOR, ROUGE_L) for each caption

make compute_evalscores

Train/Test

make train [options]
make test [options]

Please refer to the Makefile (and opts.py file) for the set of available train/test options

Examples

Train XE model

make train GID=0 EXP_NAME=xe FEATS="resnet c3d mfcc category" USE_RL=0 USE_CST=0 USE_MIXER=0 SCB_CAPTIONS=0 LOGLEVEL=DEBUG MAX_EPOCHS=50

Train CST_GT_None/WXE model

make train GID=0 EXP_NAME=WXE FEATS="resnet c3d mfcc category" USE_RL=1 USE_CST=1 USE_MIXER=0 SCB_CAPTIONS=0 LOGLEVEL=DEBUG MAX_EPOCHS=50

Train CST_MS_Greedy model (using greedy baseline)

make train GID=0 EXP_NAME=CST_MS_Greedy FEATS="resnet c3d mfcc category" USE_RL=1 USE_CST=0 SCB_CAPTIONS=0 USE_MIXER=1 MIXER_FROM=1 USE_EOS=1 LOGLEVEL=DEBUG MAX_EPOCHS=200 START_FROM=output/model/WXE

Train CST_MS_SCB model (using SCB baseline, where SCB is computed from GT captions)

make train GID=0 EXP_NAME=CST_MS_SCB FEATS="resnet c3d mfcc category" USE_RL=1 USE_CST=1 USE_MIXER=1 MIXER_FROM=1 SCB_BASELINE=1 SCB_CAPTIONS=20 USE_EOS=1 LOGLEVEL=DEBUG MAX_EPOCHS=200 START_FROM=output/model/WXE

Train CST_MS_SCB(*) model (using SCB baseline, where SCB is computed from model sampled captions)

make train GID=0 MODEL_TYPE=concat EXP_NAME=CST_MS_SCBSTAR FEATS="resnet c3d mfcc category" USE_RL=1 USE_CST=1 USE_MIXER=1 MIXER_FROM=1 SCB_BASELINE=2 SCB_CAPTIONS=20 USE_EOS=1 LOGLEVEL=DEBUG MAX_EPOCHS=200 START_FROM=output/model/WXE

If you want to change the input features, modify the FEATS variable in above commands.

Reference

@article{cst_phan2017,
    author = {Sang Phan and Gustav Eje Henter and Yusuke Miyao and Shin'ichi Satoh},
    title = {Consensus-based Sequence Training for Video Captioning},
    journal = {ArXiv e-prints},
    archivePrefix = "arXiv",
    eprint = {1712.09532},
    year = {2017},
}

Todo

  • Test on Youtube2Text dataset (different number of captions per video)

Acknowledgements

  • Torch implementation of NeuralTalk2
  • PyTorch implementation of Self-critical Sequence Training for Image Captioning (SCST)
  • PyTorch Team