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Grounding Action Descriptions in Videos
Open accessAbstract
Recent work has shown that the integration of visual information into text-based models can substantially improve model predictions, but so far only visual information extracted from static images has been used. In this paper, we consider the problem of grounding sentences describing actions in visual information extracted from videos. We present a general purpose corpus that aligns high quality videos with multiple natural language descriptions of the actions portrayed in the videos, together with an annotation of how similar the action descriptions are to each other. Experimental results demonstrate that a text-based model of similarity between actions improves substantially when combined with visual information from videos depicting the described actions.
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Glenberg, A. M., & Kaschak, M. P. (2002) · Psychonomic Bulletin & Review · doi:10.3758/bf03196313 · Cited by 2,361
Cited by
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
Xu, J., Mei, T., Yao, T., & Rui, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.571 · Cited by 1,759
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Goyal, R., Kahou, S. E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.622 · Cited by 1,471
Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., & Fox, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01075 · Cited by 540
Metadata
- Authors
- Regneri, M., Rohrbach, M., Wetzel, D., Thater, S., Schiele, B., & Pinkal, M.
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2013-12
- Volume
- 1
- Pages
- 25-36
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2111078031
- Cited by
- 467
- References
- 1
- FWCI
- 19.70
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Regneri, M., Rohrbach, M., Wetzel, D., Thater, S., Schiele, B., & Pinkal, M. (2013). Grounding Action Descriptions in Videos. Transactions of the Association for Computational Linguistics. https://doi.org/10.1162/tacl_a_00207
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
