vmr-axiou

Codes of our paper "AxIoU: Axiomatically Justified Measure for Video Moment Retrieval" (CVPR'22)

Dependencies

$ pip install -r requirements.txt
$ python -m spacy download en_core_web_sm

All codes are tested with Python3.7.7.

Data

Charades-STA

1. Download Charades annotations and save Charades_v1_train.csv and Charades_v1_test.csv in data/raw/charades/.
2. Download Charades-STA annotations. Only train and test annotation files are required.

├── data
│   ├── processed
│   └── raw
        └── charades
            └──Charades_v1_train.csv
            └──Charades_v1_test.csv
            └──charades_sta_train.txt
            └──charades_sta_test.txt

Then run these commands below:

$ python src/data/make_dataset data/raw/charades/charades_sta_train.txt data/raw/charades/Charades_v1_train.csv 
$ python src/data/make_dataset data/raw/charades/charades_sta_test.txt data/raw/charades/Charades_v1_test.csv

Dataset for Model Selection Experiments

We provide the dataset for our model selection experiments, which includes the snapshots of model predicitons for each epoch of model training with 5 different hyper-parameters.

1. Download the dataset (exp-*.tar.gz) from the release
2. Decompress them into data/validation_exp/exp-*

ActivityNet Captions

Download annotations here and save train.json, val_1.json and val_2.json in data/raw/activitynet/.

├── data
│   ├── processed
│   └── raw
        └── activitynet
            └──train.json
            └──val_1.json
            └──val_2.json

Evaluate your model's outputs

src/toolbox provides tools for evaluation and visualization of moment retrieval. For example, evaluation on Charades-STA is done as:

from src.toolbox.data_converters import CharadesSTA2Instances
from src.toolbox.eval import recall, axiou, accumulate_metrics

test_data = CharadesSTA2Instances(
    pd.read_csv(f"data/processed/charades/charades_test.csv")
)
############################
## your prediction code here
## ....
############################

recall_results = recall(test_data, predictions)
recall_summary = accumulate_metrics(recall_results)

axiou_results = axiou(test_data, predictions)
axiou_summary = accumulate_metrics(axiou_results)

predictions is a list of model's output. Each item should be in the format as:

(
 (video_id: str, description: str),
 List[(moment_start: float, moment_end: float, video_duration: float)],
 List[rating: float]
)

• video_id: video ID
• description: a query sentence.
• moment_start: a starting point of predicted moment's location in seconds
• moment_end: a end point of predicted moment's location in seconds
• video_duration: the duration of a whole video in seconds.
• rating: a score of a predicted location. A prediction with the largest rating is evaluated as top-1 prediction.

For example, an item in predictions is like:

predictions[0]

(('3MSZA', 'person turn a light on.'),
 [[0.76366093268685, 7.389522474042329, 30.96],
  [21.86557223053205, 29.71737331263709, 30.96],
  ...
  ],
 [7.252954266982226,
  4.785879048072588,
  ...])

summary is a dictionary of metrics (e.g. R@k (IoU>m) and AxIoU@k). Examples of how to use our toolbox are in the notebook of our reproducible experiments (notebooks/ExperimentsOnCharadesSTA.ipynb).

If this work helps your research, please cite:

@InProceedings{Togashi_2022_CVPR,
    author    = {Togashi, Riku and Otani, Mayu and Nakashima, Yuta and Rahtu, Esa and Heikkil\"a, Janne and Sakai, Tetsuya},
    title     = {AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {21076-21085}
}