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Action Co-occurrence Priors for HOI Detection

Official code for our ECCV 2020 paper,

Detecting Human-Object Interactions with Action Co-occurrence Priors.

Done by Dong-Jin Kim, Xiao Sun, Jinsoo Choi, Stephen Lin, and In So Kweon.

We Introduce novel "action co-occurrence priors" to improve state-of-the-art performance of Human-Object Interaction (HOI) detectors.

The figure shows the marginal/conditional probability values computed from the distribution of the training label. Intuitively, detection of rarely labeled HOIs (operate-hair dryer) can be facilitated by detection of commonly co-occurring HOIs (hold-hair dryer). Also, non-detection of rare HOIs (blow-cake) can be aided by detection of incompatible HOIs (cut-cake). We leverage this intuition as a prior to learn an HOI detector effective on long-tailed datasets.

Examples of co-occurrence matrices constructed for several objects. Along the Y-axis is the given action, and the X-axis enumerates conditional actions. Each element represents the conditional probability that an action occurs when another action is happening.

Requirements

Some of the codes are built upon "No-Frills Human-Object Interaction Detection: Factorization, Layout Encodings, and Training Techniques" [website]. We appreciate them for their great work.

All dependencies will be installed in a python3 virtual environment.

Step 1: Create a python virtual environment

conda create -n acp python=3.6

Step 2: Activate the environment

conda activate acp

Step 3: Install the dependencies

Here are the main requirements:

  • python 3.6.9
  • pytorch 1.0.0
  • numpy 1.17.3
  • tqdm 4.38.0
  • scipy 1.3.1
  • scikit-image 0.13.1
  • scikit-learn 0.19.1
  • h5py 2.9.0

Setup

We will be executing all commands inside the root directory (.../ActionCooccurrencePriors/) that was created when you cloned the repository.

To begin, we will create a directory in the root directory called data_symlinks that would contain symlinks to any data to be used or produced by our code. Specifically we will create 3 symlinks:

  • hico_clean -> directory where you downloaded HICO-Det dataset
  • hico_processed -> directory where you want to store processed data required for training/evaluating models
  • hico_exp -> directory where you want to store outputs of model training and evaluation

Creating these symlinks is useful if your hardware setup constrains where you keep your data. For example, if you want to store the dataset on the local drives, and code, processed files, and experiment data on the NFS to be shared across multiple servers

mkdir data_symlinks
cd data_symlinks
ln -s <path to hico_clean> ./hico_clean
ln -s <path to hico_processed> ./hico_processed
ln -s <path to hico_exp> ./hico_exp

If executed correctly, ls -l data_symlinks in the root directory should show something like:

hico_clean -> /data/tanmay/hico/hico_det_clean_20160224
hico_exp -> /home/nfs/tgupta6/Code/hoi_det_data/hico_exp
hico_processed -> /home/nfs/tgupta6/Code/hoi_det_data/hico_processed

Download the HICO-Det dataset

We will now download the required data from the HICO-Det website to hico_clean. Here are the links to all the files (version 0160224) you would need to download

Extract the images and annotations file which will be download as a tar.gz file using

tar xvzf <path to tar.gz file> -C <path to hico_clean directory>

Here -C flag specifies the target location where the files will be extracted.

After this step output of ls -l data_symlinks/hico_clean should look like

anno_bbox.mat
anno.mat
hico_list_hoi.txt
hico_list_obj.txt
hico_list_vb.txt
images
README
tools

Process HICO-Det files

The HICO-Det dataset consists of images and annotations stored in the form of .mat and .txt files. Run the following command to quickly convert this data into easy to understand json files which will be written to hico_processed directory

bash data/hico/process.sh

In addition, the process.sh performs the following functions:

  • It calls data/hico/split_ids.py which separates sample ids into train, val, train_val (union of train and val), and test sets.
  • It executes data/hico/hoi_cls_count.py which counts number of training samples for each HOI category

The splits are needed for both training and evaluation. Class counts are needed only for evaluation to compute mAP of group of HOI classes created based on number of available training examples.

Download Faster-RCNN detections

  • Download faster_rcnn_boxes.zip to hico_processed directory
  • Extract the file in the hico_processed directory 
    cd <path to hico_processed>
tar -xvzf faster_rcnn_boxes.tar.gz -C ./
rm faster_rcnn_boxes.tar.gz
cd <path to root>
  • Write Faster-RCNN features to an hdf5 file 
    python -m exp.hoi_classifier.data.write_faster_rcnn_feats_to_hdf5

For each image, Faster-RCNN predicts class scores (for 80 COCO classes) and box regression offsets (per class) for upto 300 regions. In this step, for each COCO class, we select upto 10 high confidence predictions per class after non-max suppression by running the following:

python -m exp.detect_coco_objects.run --exp exp_select_and_evaluate_confident_boxes_in_hico

This will create an hdf5 file called selected_coco_cls_dets.h5py in hico_exp/select_confident_boxes_in_hico directory.

The above command also performs a recall based evaluation of the object detections to see what fraction of ground truth human and object annotations are recalled by these predictions. These stats are written to the following files in the same directory:

  • eval_stats_boxes.json: All selected detections irrespective of the predicted class are used for computing recall numbers.
  • eval_stats_boxes_labels.json: Only selected detections for the corresponding class are used for computing recall.

Download the human poses

  • Download human_pose.tar.gz to hico_processed directory
  • Extract the file in the hico_processed directory 
    cd <path to hico_processed>
tar -xvzf human_pose.tar.gz -C ./
rm human_pose.tar.gz
cd <path to root>

Download the co-occurrence matrices and word vectors

We provide co-occurrence matrices we constructed (both positive and negative).

We additionally provide an example of action-to-anchor mapping for training.

Finally, we provide word2vec encoder based on Glove representation for Funtional Generalization (Bansal et al., AAAI2020) we implemented.

Train HOI classifier

Step 1: Generate HOI candidates from object candidates and cache Box and Pose features

We provide a simple bash script for this:

bash exp/hoi_classifier/scripts/preprocess.sh

This generates the following files in hico_exp/hoi_candidates directory:

  • hoi_candidates_<subset>.hdf5 : Box pair candidates.
  • hoi_candidate_labels_<subset>.hdf5 : Binary labels for hoi candidates to be used during training
  • hoi_candidates_box_feats_<subset>.hdf5 : Cached Box features
  • hoi_candidates_pose_<subset>.hdf5 : Pose keypoints assigned to human bounding boxes
  • hoi_candidates_pose_feats_<subset>.hdf5 : Cached Pose features

Step 2: Train the model

Modify flags in exp/hoi_classifier/scripts/train.sh as required and run:

bash bash exp/hoi_classifier/scripts/train.sh <GPU ID>

<GPU ID> specifies the GPU to use for training the model.

This creates a directory called factors_rcnn_det_prob_appearance_boxes_and_object_label_human_pose in hico_exp/hoi_classifier. The name of the directory is automatically constructed based on the factors used in this model. The factors are enabled using appropriate flags in the train.sh file.

This directory is used to store the following:

  • constants needed for running the experiment
    • data paths (data_train/val_constants.json)
    • model hyperparameters (model_constants.json and model.txt)
    • training hyperparameters (exp_constants.json)
  • tensorboard log files (log/)
  • model checkpoints (models/)

Time and Memory

  • The full model achieves best val set performance in about 60000 iterations in ~12hrs (Nvidia K40 GPU)
  • GPU memory usage is about 6 GB

Evaluate Model

trained_models: This directory contains the selected checkpoint for our full model (with all factors). You may follow exp/hoi_classifier/eval.py script and the corresponding experiment launcher exp_eval() in exp/hoi_classifier/run.py to see how to load and use the trained model.

Step 1: Select the model to evaluate

The model can be selected based on the validation loss logged in tensorboard and is usually around 60000 iterations. Let us call the selected iteration <MODEL NUM>

Step 2: Make predictions for the test set

bash exp/hoi_classifier/scripts/eval.sh <GPU ID> <MODEL NUM>

This generates a pred_hoi_dets.hdf5 file.

Step 3: Compute mAPs

This is done by the compute_map.sh script in exp/hico_eval directory. Update variable EXP_NAME to the one you want to evaluate and MODEL_NUM to the selected <MODEL NUM> and run

bash exp/hico_eval/compute_map.sh

EXP_NAME defaults to factors_rcnn_det_prob_appearance_boxes_and_object_label_human_pose which is the model trained with all factors. The APs for each HOI category and overall performance are stored in the experiment directory with a relative path that looks like mAP_eval/test_<MODEL NUM>/mAP.json

The mAP for the provided model for various category groups (based on number of training samples) are as follows:

Model Full Rare Non-Rare
HO-RCNN [1] 7.81 5.37 8.54
InteractNet [2] 9.94 7.16 10.77
GPNN [4] 13.11 9.34 14.23
iCAN [3] 14.84 10.45 16.15
Interactiveness Prior [5] 17.22 13.51 18.32
Contextual Attention [6] 16.24 11.16 17.75
No-Frills [7] 17.07 11.5 18.74
RPNN [8] 17.35 12.78 18.71
PMFNet [9] 17.46 15.65 18.00
ACP (Ours) 20.59 15.92 21.98

References:

[1] Chao, Y., Liu, Y., Liu, X., Zeng, H., & Deng, J. (2018). Learning to Detect Human-Object Interactions. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 381-389.

[2] Gkioxari, G., Girshick, R.B., Dollár, P., & He, K. (2018). Detecting and Recognizing Human-Object Interactions. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8359-8367.

[3] Gao, C., Zou, Y., & Huang, J. (2018). iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection. BMVC.

[4] Qi, S., Wang, W., Jia, B., Shen, J., & Zhu, S. (2018). Learning Human-Object Interactions by Graph Parsing Neural Networks. ECCV.

[5] Li, Y. L., Zhou, S., Huang, X., Xu, L., Ma, Z., Fang, H. S., ... & Lu, C. (2019). Transferable interactiveness knowledge for human-object interaction detection. CVPR.

[6] Wang, T., Anwer, R. M., Khan, M. H., Khan, F. S., Pang, Y., Shao, L., & Laaksonen, J. (2019). Deep contextual attention for human-object interaction detection. ICCV.

[7] Gupta, T., Schwing, A., & Hoiem, D. (2019). No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. ICCV.

[8] Zhou, P., & Chi, M. (2019). Relation parsing neural network for human-object interaction detection. ICCV.

[9] Wan, B., Zhou, D., Liu, Y., Li, R., & He, X. (2019). Pose-aware multi-level feature network for human object interaction detection. ICCV.

Citation

Citation

If you find our work useful in your research, please consider citing our ECCV2020 paper or our TIP2021 version paper:

@inproceedings{kim2020detecting,
  title={Detecting human-object interactions with action co-occurrence priors},
  author={Kim, Dong-Jin and Sun, Xiao and Choi, Jinsoo and Lin, Stephen and Kweon, In So},
  booktitle={European Conference on Computer Vision},
  pages={718--736},
  year={2020},
  organization={Springer}
}

@article{kim2021acp++,
  title={ACP++: Action Co-Occurrence Priors for Human-Object Interaction Detection},
  author={Kim, Dong-Jin and Sun, Xiao and Choi, Jinsoo and Lin, Stephen and Kweon, In So},
  journal={IEEE Transactions on Image Processing},
  volume={30},
  pages={9150--9163},
  year={2021},
  publisher={IEEE}
}

If you have any questions about this code, feel free to contact the first author (djnjusa [at] kaist.ac.kr).