The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning Variable-Length Markov Models of Behavior
Overview
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.
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
Data Compression Using Dynamic Markov Modelling
Cormack, G. V., & Horspool, R. N. S. (1987) · The Computer Journal · doi:10.1093/comjnl/30.6.541 · Cited by 213
Cited by
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
Li, R., Yang, S., Ross, D. A., & Kanazawa, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01315 · Cited by 553
“Earlier works employ statistical models such as kernel-based probability distribution [64, 10, 25, 11] to synthesize motion, but abstract away motion details.”
MethodTrajectory-Based Anomalous Event Detection
Piciarelli, C., Micheloni, C., & Foresti, G. L. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.2005599 · Cited by 532
Planning-based prediction for pedestrians
Ziebart, B. D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J. A., Hebert, M., Dey, A. K., & Srinivasa, S. (2009) · 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2009.5354147 · Cited by 456
Learning Motion Patterns of People for Compliant Robot Motion
Bennewitz, M., Burgard, W., Cielniak, G., & Thrun, S. (2005) · The International Journal of Robotics Research · doi:10.1177/0278364904048962 · Cited by 399
Layered representations for learning and inferring office activity from multiple sensory channels
Oliver, N., Garg, A., & Horvitz, E. (2004) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.02.004 · Cited by 316
Metadata
- Authors
- Galata, A., Johnson, N., & Hogg, D.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2001-03
- Volume
- 81
- Issue
- 3
- Pages
- 398-413
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W1991366868
- Cited by
- 265
- References
- 1
- FWCI
- 22.03
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
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.
