Skip to content

Latest commit

 

History

History
75 lines (56 loc) · 3.64 KB

README.md

File metadata and controls

75 lines (56 loc) · 3.64 KB
Raw

Adversarial Inference for Multi-Sentence Video Descriptions

This is the implementation of Adversarial Inference for Multi-Sentence Video Descriptions

This repository is based on self-critical.pytorch. Thank you Ruotian for the code! The modifications are:

  • Training Multimodal Generator and Hybrid Discriminator in models/.
  • Adversarial Inference in eval_utils.py

Requirements

Clone the repository recursively. git clone --recursive https://github.com/jamespark3922/adv-inf

Python 2.7 (because there is no coco-caption version for python 3)
PyTorch 0.4 (along with torchvision)
densevid_eval (for activitynet evaluation)
java to run meteor.jar file

Training on ActivityNet Dense Captions

Download ActivityNet captions and preprocess them

We share the input labels and features in this folder. (Scripts to preprocess the labels will be available soon.)

Features

  • renext101-64f (126GB) extracted from r3d repository
  • resnet152 (14GB), extracted 100 frames for each video
  • bottomup labels (16GB) with confidence score, extracted 3 frames for each clip

After downloading them all, unzip them to your preferred feature directory.

Note that mean-pooling operations are done when loading the data in dataloader.py

Training

python train.py --caption_model video --input_json activity_net/inputs/video_data_dense.json --input_fc_dir activity_net/feats/resnext101-64f/ --input_img_dir activity_net/feats/resnet152/ --input_box_dir activity_net/feats/bottomup/ --input_label_h5 activity_net/inputs/video_data_dense_label.h5 --glove_npy activity_net/inputs/glove.npy --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 0 --checkpoint_path video_ckpt --val_videos_use -1 --losses_print_every 10 --batch_size 16 --language_eval 1

Context: The generator model uses the hidden state of previous sentence as "context", starting at epoch --g_context_epoch.

Evaluation

After training is done, evaluate the captions in paragraph level. Note the evaluation is done on val1 set.

The normal inference using greedymax or beamsearch can be run with the following command:

python eval.py --g_model_path video_ckpt/gen_best.pth --infos_path video_ckpt/infos.pkl --d_model_path video_ckpt/dis_best.pth --sample_max 1 --id $id --beam_size $beam_size

and will be saved in densevid_eval/caption_$id.json. You can also disable --d_model_path if you do not wish to score and evaluate the discriminator.

Adversarial Inference

Sampling $num_samples sentences and choosing the best one with discriminator can be run with

python eval.py --g_model_path video_ckpt/gen_best.pth --infos_path video_ckpt/infos.pkl --d_model_path video_ckpt/dis_best.pth --sample_max 0 --num_samples $num_samples --temperature $temperature --id $id

Generated Catpions

You can run the language metrics to reproduce the results

python para-evaluate.py -s $submission_file --verbose

and the diversity metrics (Div-N, Re-N) in paper.

python evaluateCaptionsDiversity.py $submission_file

Reference

@article{park2019advinf,
  title= Adversarial Inference for Multi-Sentence Video Descriptions,
  author={Park, Jae Sung and Rohrbach, Marcus and Darrell, Trevor and Rohrbach, Anna},
  jorunal={CVPR 2019},
  year={2019}
}