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A Pytorch implemention for some state-of-the-art models for" Temporally Language Grounding in Untrimmed Videos"

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Temporally-language-grounding

A Pytorch implemention for some state-of-the-art models for "Temporally language grounding in untrimmed videos"

Requirements

  • Python 2.7
  • Pytorch 0.4.1
  • matplotlib
  • The code is for Charades-STA dataset.

Three Models for this task

Supervised Learning based methods

  • TALL: Temporal Activity Localization via Language Query
  • MAC: MAC: Mining Activity Concepts for Language-based Temporal Localization.

Reinforcement Learning based method

  • A2C: Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos.

Performance

Methods R@1, IoU0.7 R@1, IoU0.5 R@5, IoU0.7 R@5, IoU0.5
TALL 8.63 24.09 29.33 59.60
MAC 12.31 29.68 37.31 64.14
A2C 14.25 32.66 None None

Features Download

Training and Testing

Training and Testing for TALL, run

python main_charades_SL.py --model TALL

Training and Testing for MAC, run

python main_charades_SL.py --model MAC

Training and Testing for A2C, run

python main_charades_RL.py

Acknowledgements

Thanks the original TALL, MAC and awesome PyTorch team.

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A Pytorch implemention for some state-of-the-art models for" Temporally Language Grounding in Untrimmed Videos"

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