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Accepted by ICCV2023, Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach

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Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach

PWC PWC PWC

The code is assembled to OpenWTAL, which implements multiple WTAL methods in a unified codebase.

Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach
Qinying Liu, Zilei Wang, Shenghai Rong, Junjie Li, Yixin Zhang
ICCV2023

[Paper]

Data Preparation

  1. Download the features of THUMOS14 from dataset.zip.
  2. Place the features inside the ./data folder.

Train and Evaluate

  1. Train the CASE model by run
    python main_case.py --exp_name CASE
    
  2. The pre-trained model will be saved in the ./outputs folder. You can evaluate the model by running the command below.
    python main_case.py --exp_name CASE --inference_only
    
    We provide our pre-trained checkpoints in checkpoints.zip

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Accepted by ICCV2023, Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach

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