Explore-And-Match

Implementation of "Explore-And-Match".

Getting Started

⚠️ Dependencies:

• cuda == 10.2
• torch == 1.8.0
• torchvision == 0.9.0
• python == 3.8.11
• numpy == 1.20.3

Dataset Preparation

• ActivityNet
• Charades

split

• ActivityNet (train/val/test)
• Charades (train/test: 5338/1334)

Download ActivityNet

• Request videos: you need to request download with below form

  https://docs.google.com/forms/d/e/1FAIpQLSeKaFq9ZfcmZ7W0B0PbEhfbTHY41GeEgwsa7WobJgGUhn4DTQ/viewform

merge 'v1-2' and 'v1-3' into a single folder 'videos'.

• Download annotations (ActivityNet Captions)

  https://cs.stanford.edu/people/ranjaykrishna/densevid/captions.zip

Download Charades

• Download videos

  https://prior.allenai.org/projects/charades

• Download annotations

  https://github.com/jiyanggao/TALL

Pre-trained features

• C3D
• CLIP

Preprocess

Get 64/128/256 frames per video:

bash preprocess/get_constant_frames_per_video.sh

Extract features with CLIP

change 'val_1' to 'val' and 'val_2' to 'test' CLIP encodings

bash preprocess/get_clip_features.sh

Train

activitynet, charades

bash train_{dataset}.sh

Evaluation

bash test_{dataset}.sh

Configurations

refer to lib/configs.py

Citation

@article{woo2022explore,
  title={Explore and Match: End-to-End Video Grounding with Transformer},
  author={Woo, Sangmin and Park, Jinyoung and Koo, Inyong and Lee, Sumin and Jeong, Minki and Kim, Changick},
  journal={arXiv preprint arXiv:2201.10168},
  year={2022}
}