bpiyush/chirality-in-action

Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening

ArXiv: https://arxiv.org/abs/2509.08502

We provide all the splits for the three datasets uses in chiral action recognition proposed in the paper.

The description of each split is as follows:

Split	Description
chiral_groups-combined.csv	Combined chiral groups for all datasets
chiral_groups-ssv2-train.csv	Chiral groups for SSv2 train set
chiral_groups-charades-train.csv	Chiral groups for Charades train set
chiral_groups-epic-train.csv	Chiral groups for Epic train set
chiral_groups-ssv2-validation.csv	Chiral groups for SSv2 validation set
chiral_groups-charades-validation.csv	Chiral groups for Charades validation set
chiral_groups-epic-validation.csv	Chiral groups for Epic validation set
cia-ssv2-train.csv	Chiral groups for SSV2 train set
cia-ssv2-validation.csv	Chiral groups for SSV2 validation set
cia-charades-train.csv	Chiral groups for Charades train set
cia-charades-validation.csv	Chiral groups for Charades validation set
cia-epic-train.csv	Chiral groups for Epic train set
cia-epic-validation.csv	Chiral groups for Epic validation set

The splits with prefix chiral_groups contain chiral groups: (verb positive, verb negative, noun) triplets. The splits with prefix cia contain the train and validation splits for each dataset with only the subset of the dataset relevant for chiral action recognition.

If you have any questions, please contact us via email.

Citation

If you use this dataset in your research, please cite the following paper:

@article{bagad2025chirality,
  title={Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening},
  author={Bagad, Piyush and Zisserman, Andrew},
  journal={arXiv preprint arXiv:2509.08502},
  year={2025}
}