The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Max-Margin Early Event Detectors
Overview
The claims and statistics of this paper haven’t been checked yet.
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Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303
(1992) · doi:10.5555/176313.176316 · Cited by 2,892
Detecting depression from facial actions and vocal prosody
Cohn, J. F., Kruez, T. S., Matthews, I., Yang, Y., Nguyen, M. H., Padilla, M. T., Zhou, F., & De la Torre, F. (2009) · 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops · doi:10.1109/acii.2009.5349358 · Cited by 494
Cited by
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response
Koppula, H. S., & Saxena, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2430335 · Cited by 596
Decomposing Motion and Content for Natural Video Sequence Prediction
Villegas, R., Yang, J., Hong, S., Lin, X., & Lee, H. (2017) · arXiv · doi:10.48550/arxiv.1706.08033 · Cited by 488
Furnari, A., & Farinella, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00635 · Cited by 182
“To assess the timeliness of anticipations, we design a new evaluation measure inspired by the AMOC curve [23].”
BackgroundMethodRFID based real-time recognition of ongoing gesture with adversarial learning
Yu, Y., Wang, D., Zhao, R., & Zhang, Q. (2019) · Proceedings of the 17th Conference on Embedded Networked Sensor Systems · doi:10.1145/3356250.3360045 · Cited by 68
Multi-Modality Multi-Task Recurrent Neural Network for Online Action Detection
Liu, J., Li, Y., Song, S., Xing, J., Lan, C., & Zeng, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2799968 · Cited by 62
Metadata
- Authors
- Hoai, M., & De la Torre, F.
- Venue
- International Journal of Computer Vision
- Published
- 2013-12-13
- Volume
- 107
- Issue
- 2
- Pages
- 191-202
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2016776918
- Cited by
- 212
- References
- 3
- FWCI
- 15.24
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Max-Margin Early Event Detectors. https://app.cochat.ai/papers/10.1007/s11263-013-0683-3
- The paper
- Hoai, M., & De la Torre, F. (2014). Max-Margin Early Event Detectors. International Journal of Computer Vision. https://doi.org/10.1007/s11263-013-0683-3
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
