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Refining Boundary Head

This repository implements the boundaries head proposed in the paper:

Hanyuan Wang, Majid Mirmehdi, Dima Damen, Toby Perrett, Refining Action Boundaries for One-stage Detection, AVSS, 2022

[arXiv paper]

This repository is based on ActionFormer.

Citing

When using this code, kindly reference:

@INPROCEEDINGS{HanyuanRefining2022,
  author={Wang, Hanyuan and Mirmehdi, Majid and Damen, Dima and Perrett, Toby}
  booktitle={The 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)},
  title={Refining Action Boundaries for One-stage Detection},
  year={2022}}

Dependencies

  • Python 3.5+
  • PyTorch 1.11
  • CUDA 11.0+
  • GCC 4.9+
  • TensorBoard
  • NumPy 1.11+
  • PyYaml
  • Pandas
  • h5py
  • joblib

Complie NMS code by:

cd ./libs/utils
python setup.py install --user
cd ../..

Preparation

Datasets and feature

You can download the annotation repository of EPIC-KITCHENS-100 at here. Place it into a folder: ./data/epic_kitchens/annotations.

You can download the videos of EPIC-KITCHENS-100 at here.

You can download the feature on EPIC-KITCHENS-100 at here. Place it into a folder: ./data/epic_kitchens/features.

If everything goes well, you can get the folder architecture of ./data like this:

data                       
└── epic_kitchens                    
    ├── features              
    └── annotations

Pretrained models

You can download our pretrained models on EPIC-KITCHENS-100 at here.

Training/validation on EPIC-KITCHENS-100

To train the model run:

python ./train.py ./configs/epic_slowfast.yaml --output reproduce  --gau_sigma 5.5 --sigma1 0.5 --sigma2 0.5 --verb_cls_weight 0.5 --noun_cls_weight 0.5

To validate the model run:

python ./eval.py ./configs/epic_slowfast.yaml ./ckpt/epic_slowfast_reproduce/name_of_the_best_model --gau_sigma 5.5

Results

[RESULTS] Action detection results_self.ap_action

|tIoU = 0.10: mAP = 19.19 (%)
|tIoU = 0.20: mAP = 18.61 (%)
|tIoU = 0.30: mAP = 17.47 (%)
|tIoU = 0.40: mAP = 16.30 (%)
|tIoU = 0.50: mAP = 14.33 (%)
Avearge mAP: 17.18 (%)
[RESULTS] Action detection results_self.ap_noun

|tIoU = 0.10: mAP = 23.58 (%)
|tIoU = 0.20: mAP = 22.40 (%)
|tIoU = 0.30: mAP = 21.03 (%)
|tIoU = 0.40: mAP = 19.27 (%)
|tIoU = 0.50: mAP = 16.39 (%)
Avearge mAP: 20.53 (%)
[RESULTS] Action detection results_self.ap_verb

|tIoU = 0.10: mAP = 23.75 (%)
|tIoU = 0.20: mAP = 22.68 (%)
|tIoU = 0.30: mAP = 21.22 (%)
|tIoU = 0.40: mAP = 19.19 (%)
|tIoU = 0.50: mAP = 16.73 (%)
Avearge mAP: 20.71 (%)

Reference

This implementation is based on ActionFormer.

Our main contribution is in:

./libs/modeling/meta_archs.:
* We incorporate the estimation of boundary confidence into prediction heads in both training and inference. 
* We merged the classification heads of verb and noun, so the model can predict results for action task. 
* We implemented label assignment for boundary confidence.

./libs/modeling/losses.py:
* We added the supervision of boundary confidence, including confidence scaling and loss function calculation.

libs/datasets/epic_kitchens.py:
* We Loaded data for noun and verb together. 

/libs/utils/nms.py:
* We changed the sort of NMS through action socres instead of separate verb/noun scores.