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A PyTorch implementation of RSC based on MMM 2023 paper "Weakly-supervised Temporal Action Localization with Regional Similarity Consistency"

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RSC

A PyTorch implementation of RSC based on MMM 2023 paper Weakly-supervised Temporal Action Localization with Regional Similarity Consistency.

Network Architecture

Usage

Git clone the corresponding repos and replace the files provided by us, then run the code according to readme of corresponding repos.

For example, to train HAM-Net on THUMOS14 dataset:

git clone https://github.com/asrafulashiq/hamnet.git
mv AGCT/hamnet/* hamnet/
python main.py

To evaluate HAM-Net on THUMOS14 dataset:

python main.py --test --ckpt [checkpoint_path]

Benchmarks

The models are trained on one NVIDIA GeForce GTX 1080 Ti (11G). All the hyper-parameters are the default values. Here we provide the pre-trained models on THUMOS14 dataset.

THUMOS14

Method THUMOS14 Download
mAP@0.1 mAP@0.2 mAP@0.3 mAP@0.4 mAP@0.5 mAP@0.6 mAP@0.7 mAP@AVG
HAM-Net 66.9 60.2 51.0 42.0 31.7 22.1 12.0 40.9 OneDrive
CoLA 67.2 61.5 52.9 43.9 34.8 24.9 13.0 42.6 OneDrive
CO2-Net 70.6 64.2 55.9 47.7 38.9 26.0 13.6 45.3 OneDrive

mAP@AVG is the average mAP under the thresholds 0.1:0.1:0.7.

Results

vis

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A PyTorch implementation of RSC based on MMM 2023 paper "Weakly-supervised Temporal Action Localization with Regional Similarity Consistency"

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