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BaFormer

This is the official implementation for the paper: "Efficient Temporal Action Segmentation via Boundary-aware Query Voting". arXiv NeurIPS

📄 arXiv | 🧠 NeurIPS

🚀 Overview

BaFormer delivers competitive results while requiring less FLOPs and running time. Moreover, our query-based voting mechanism significantly reduces inference time required by the single-stage model.

Figure 1: Accuray vs. inference time on 50Salads. The bubble size represents the FLOPs in inference. Under different backbones, BaFormer enjoys the benefit of boundary-aware query voting with less running time and improved accuracy.

Figure 2: It predicts query classes and masks, along with boundaries from output heads. Although each layer in the Transformer decoder holds three heads, we illustrate the three heads in the last layer for simplicity.

🛠️ Installation

# 1. Clone the repo
git clone https://github.com/peiyao-w/BaFormer.git
cd BaFormer

# 2. Create environment
conda create -n baformer python=3.8 -y
conda activate baformer

# 3. Install dependencies
pip install -r requirements.txt

Dataset Preparation

(1) To obtain the features and ground-truth annotations, please download the data as described in Data Preparation, and organize it according to the following structure. Then update the data path in the code to match the location of your downloaded dataset.

data/
 ├── 50salads/
 │    ├── mapping.txt/
 │    └── splits/
 │          ├── train.split1.bundle
 │          ├── train.split2.bundle
 │          ├── train.split3.bundle
 │          ├── train.split4.bundle
 │          ├── train.split5.bundle
 │          ├── test.split1.bundle
 │          ├── test.split2.bundle
 │          ├── test.split3.bundle
 │          ├── test.split4.bundle
 │          └── test.split5.bundle
 │    └── groundTruth/
 │    └── feature/
 ├── gtea/
 └── breakfast/

🧠 Training

To train BaFormer on a selected dataset, you can specify the dataset name, split, and path using the following arguments:

python main.py --config configs/framed_en_de.yaml  dataset.name 50salads dataset.split 1 dataset.dataset_dir /path/to/your_data/

Here:

  • dataset.name defines which dataset to use (e.g., gtea, breakfast, or 50salads).
  • dataset.split specifies the split index for training/testing.
  • dataset.dataset_dir points to the root directory where your dataset is stored.

📈 Evaluation

After training the model, you can evaluate BaFormer on the test set using the following command:

python Inference.py --config configs/framed_en_de.yaml --checkpoint experiment/checkpoints/model_best.pth dataset.name 50salads dataset.split 1 dataset.dataset_dir /path/to/your_data/

📂 Checkpoints and Logs

Checkpoints and logs will be automatically saved under:

./experiments/
 ├── 50salads/
 │      └── bk_fde_tde/final/
 │           ├── 1/
 │               ├── checkpoint_best.pth
 │               ├── log_plain.txt
 │               ├── log.txt
 │               ├── last_checkpoint
 │               ├── config.yaml
 │               ├── config_min.yaml
 │               ├── env.yaml
 │               └── logs_epoch/
 │           ├── 2/
              ....

 ├── gtea/
 └── breakfast/

Our trained checkpoints are provided below. Please place them in the experiments/final/ directory before running evaluation.

📄 Other

Citation

@article{wang2024efficient,
  title={Efficient temporal action segmentation via boundary-aware query voting},
  author={Wang, Peiyao and Lin, Yuewei and Blasch, Erik and Ling, Haibin and others},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={37765--37790},
  year={2024}
}