This repository contrains code for the TPAMI paper Multi-Task Learning of Object States and State-Modifying Actions from Web Videos.

1. Setup the environment

   • Our code can be run in a docker container. Build it by running the following command. Note that by default, we compile custom CUDA code for architectures 6.1, 7.0, 7.5, 8.0, and 8.6. You may need to update the Dockerfile with your GPU architecture.

     docker build -t multi-task-object-states .
     

   • Go into the docker image.

     docker run -it --rm --gpus all -v $(pwd):$(pwd) -w $(pwd) --user=$(id -u $USER):$(id -g $USER) multi-task-object-states bash
     

2. Download requirements

   • Our code requires CLIP repository, CLIP model weights, and the ChangeIt dataset annotations. Run ./download_requirements.sh to obtain those dependencies or download them yourselves.

3. Download dataset

   • To replicate our experiments on the ChangeIt dataset, the dataset videos are required. Please download them and put them inside videos/*category* folder. See ChangeIt GitHub page on how to download them.

4. Train a model

   • Run the training.

     python train.py --video_roots ./videos
                     --dataset_root ./ChangeIt
                     --train_backbone
                     --augment
                     --local_batch_size 2
     

   • We trained the model on 32 GPUs, i.e. batch size 64.
   • To run the code on multiple GPUs, simply run the code on a machine with multiple GPUs.
   • To run the code on multiple nodes, run the code once on each node. If you are not running on slurm, you also need to set environment variable SLURM_NPROCS to the total number of nodes and the variable SLURM_PROCID to the node id starting from zero. Make sure you also set SLURM_JOBID to some unique value.

• To train the model on your dataset, complete steps 1. and 2. from above.
• Put your videos into *dir*/*category* for every video category *category*.
• Put your annotations for selected videos into *dataset*/annotations/*category*. Use the same format as in the case of ChangeIt dataset.
• Run the training.

  python train.py --video_roots *dir*
                  --dataset_root *dataset*
                  --train_backbone
                  --augment
                  --local_batch_size 2
                  --ignore_video_weight
  

• --ignore_video_weight option ignores noise adaptive weighting done for noisy ChangeIt dataset. To use the noise adaptive weighting, you need to provide *dataset*/categories.csv and *dataset*/videos/*category*.csv files as well.

Here is an example code for the inference of a trained model.

checkpoint = torch.load("path/to/saved/model.pth", map_location="cpu")
model = ClipClassifier(params=checkpoint["args"],
                       n_classes=checkpoint["n_classes"],
                       hidden_mlp_layers=checkpoint["hidden_mlp_layers"]).cuda()
model.load_state_dict({k.replace("module.", ""): v for k, v in checkpoint["state_dict"].items()})

video_frames = torch.from_numpy(
    extract_frames(video_fn, fps=1, size=(398, 224), crop=(398 - 224, 0)))

with torch.no_grad():
    predictions = model(video_frames.cuda())
state_pred, action_pred = torch.softmax(predictions["state"], -1), torch.softmax(predictions["action"], -1)

@article{soucek2024multitask,
    title={Multi-Task Learning of Object States and State-Modifying Actions from Web Videos},
    author={Sou\v{c}ek, Tom\'{a}\v{s} and Alayrac, Jean-Baptiste and Miech, Antoine and Laptev, Ivan and Sivic, Josef},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year={2024},
    doi={10.1109/TPAMI.2024.3362288}
}

This work was partly supported by the European Regional Development Fund under the project IMPACT (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000468), the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140), the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR19-P3IA-0001 (PRAIRIE 3IA Institute), and Louis Vuitton ENS Chair on Artificial Intelligence.

The ordering constraint code has been adapted from the CVPR 2022 paper Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos available on GitHub.