See also our new paper on object state and action discovery here!

Abstract

Human actions often induce changes of object states such as “cutting an apple”, “cleaning shoes” or “pouring coffee”. In this paper, we seek to temporally localize object states (e.g. “empty” and “full” cup) together with the corresponding state-modifying actions (“pouring coffee”) in long uncurated videos with minimal supervision. The contributions of this work are threefold. First, we develop a self-supervised model for jointly learning state-modifying actions together with the corresponding object states from an uncurated set of videos from the Internet. The model is self-supervised by the causal ordering signal, i.e. initial object state → manipulating action → end state. Second, to cope with noisy uncurated training data, our model incorporates a noise adaptive weighting module supervised by a small number of annotated still images, that allows to efficiently filter out irrelevant videos during training. Third, we collect a new dataset with more than 2600 hours of video and 34 thousand changes of object states, and manually annotate a part of this data to validate our approach. Our results demonstrate substantial improvements over prior work in both action and object state-recognition in video.

CVPR 2022 Presentation

Check out our CVPR 2022 poster here!

Example Model Predictions

Test it on your videos! Instructions and trained model weights are available at our GitHub page.

Dataset

Information on how to download the ChangeIt dataset is available at its GitHub page.

Citation

@inproceedings{soucek2022lookforthechange,
    title={Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos},
    author={Sou\v{c}ek, Tom\'{a}\v{s} and Alayrac, Jean-Baptiste and Miech, Antoine and Laptev, Ivan and Sivic, Josef},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month={June},
    year={2022}
}

Acknowledgements

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. We would like to also thank Kateřina Součková and Lukáš Kořínek for their help with the dataset.