The DOI matches its Crossref record; 47 of the 50 references it cites were found in a registry or an index.
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
Open accessOverview
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“recurrent models [27, 9], which can capture latent temporal patterns but are difficult to interpret”
From Temporal Convolutional Networks for Action Segmentation and Detection (2017) · cites this paper as [9]
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
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Susstrunk, S. E. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.120 · Cited by 9,451
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
Self-citationFramewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152
Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Self-citationUcf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., & Shah, M. (2012) · arXiv · doi:10.48550/arxiv.1212.0402 · Cited by 4,575
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587756 · Cited by 3,481
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
ActivityNet: A large-scale video benchmark for human activity understanding
Heilbron, F. C., Escorcia, V., Ghanem, B., & Niebles, J. C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298698 · Cited by 2,578
Self-citationZhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.11 · Cited by 2,264
Towards End-To-End Speech Recognition with Recurrent Neural Networks
Graves, A., & Jaitly, N. (2014) · Cited by 1,861
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
Niebles, J. C., Wang, H., & Fei-Fei, L. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0122-4 · Cited by 1,475
Self-citationSequence to Sequence -- Video to Text
Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R. J., Darrell, T., & Saenko, K. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.515 · Cited by 1,448
Convolutional Learning of Spatio-temporal Features
Taylor, G. W., Fergus, R., LeCun, Y., & Bregler, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_11 · Cited by 652
End-to-End Learning of Action Detection from Frame Glimpses in Videos
Yeung, S., Russakovsky, O., Mori, G., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.293 · Cited by 585
Self-citationRyoo, M. S., & Aggarwal, J. K. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459361 · Cited by 582
Modeling video evolution for action recognition
Fernando, B., Gavves, E., Jose Oramas, M., Ghodrati, A., & Tuytelaars, T. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299176 · Cited by 573
The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities
Kuehne, H., Arslan, A., & Serre, T. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.105 · Cited by 571
Gkioxari, G., & Malik, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298676 · Cited by 552
A database for fine grained activity detection of cooking activities
Rohrbach, M., Amin, S., Andriluka, M., & Schiele, B. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247801 · Cited by 521
Mind's eye: A recurrent visual representation for image caption generation
Chen, X., & Zitnick, C. L. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298856 · Cited by 498
Activity recognition using the velocity histories of tracked keypoints
Messing, R., Pal, C., & Kautz, H. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459154 · Cited by 457
Learning latent temporal structure for complex event detection
Tang, K., Fei-Fei, L., & Koller, D. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247808 · Cited by 359
Self-citationAutomatic annotation of human actions in video
Duchenne, O., Laptev, I., Sivic, J., Bach, F., & Ponce, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459279 · Cited by 275
Weakly Supervised Action Labeling in Videos under Ordering Constraints
Bojanowski, P., Lajugie, R., Bach, F., Laptev, I., Ponce, J., Schmid, C., & Šivic, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_41 · Cited by 214
An end-to-end generative framework for video segmentation and recognition
Kuehne, H., Gall, J., & Serre, T. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477701 · Cited by 188
Parsing Videos of Actions with Segmental Grammars
Pirsiavash, H., & Ramanan, D. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.85 · Cited by 160
What’s Cookin’? Interpreting Cooking Videos using Text, Speech and Vision
Malmaud, J., Huang, J., Rathod, V., Johnston, N., Rabinovich, A., & Murphy, K. P. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1015 · Cited by 159
Finding Actors and Actions in Movies
Bojanowski, P., Bach, F., Ivan Laptev, A., Ponce, J., Schmid, C., & Šivic, J. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.283 · Cited by 128
Watch-n-patch: Unsupervised understanding of actions and relations
Wu, C., Zhang, J., Savarese, S., & Saxena, A. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299065 · Cited by 126
Weakly-Supervised Alignment of Video with Text
Bojanowski, P., Lajugie, R., Grave, É., Bach, F., Ivan Laptev, A., Ponce, J., & Schmid, C. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.507 · Cited by 124
Action Recognition by Hierarchical Sequence Summarization
Song, Y., Morency, L., & Davis, R. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.457 · Cited by 97
Linking People in Videos with “Their” Names Using Coreference Resolution
Ramanathan, V., Joulin, A., Liang, P., & Fei-Fei, L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_7 · Cited by 90
Self-citationUnsupervised Semantic Parsing of Video Collections
Şener, O., Zamir, A., Savarese, S., & Saxena, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.509 · Cited by 89
Discriminative Hierarchical Modeling of Spatio-temporally Composable Human Activities
Lillo, I., Soto, Á., & Niebles, J. C. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.109 · Cited by 87
Self-citationFrom Stochastic Grammar to Bayes Network: Probabilistic Parsing of Complex Activity
Vo, N., & Bobick, A. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.338 · Cited by 76
Learning Temporal Embeddings for Complex Video Analysis
Ramanathan, V., Tang, K., Mori, G., & Fei-Fei, L. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.508 · Cited by 73
Self-citationAction Recognition by Hierarchical Mid-Level Action Elements
Lan, T., Zhu, Y., Zamir, A., & Savarese, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.517 · Cited by 66
Instructional Videos for Unsupervised Harvesting and Learning of Action Examples
Yu, S.-I., Jiang, L., & Hauptmann, A. G. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654997 · Cited by 51
Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention MechanismPreprint
Li, Y., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., & Courville, A. C. (2015) · arXiv (Cornell University) · Cited by 43
Xu, Z., Zhu, L., Yang, Y., & Hauptmann, A. G. (2015) · Cited by 9
Research works printed but not linked: 3
- Alayrac, J.B., Bojanowski, P., Agrawal, N., Sivic, J., Laptev, I., Lacoste-Julien, S.: Learning from narrated instruction videos (2015). arXiv preprint arXiv:1506.09215 · arXiv
- Ng, J.Y.H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification (2015). arXiv preprint arXiv:1503.08909 · arXiv
- Yeung, S., Russakovsky, O., Jin, N., Andriluka, M., Mori, G., Fei-Fei, L.: Every moment counts: dense detailed labeling of actions in complex videos (2015). arXiv preprint arXiv:1507.05738 · arXiv
Cited by
Sorted by citations
Temporal Convolutional Networks for Action Segmentation and Detection
Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.113 · Cited by 2,238
“In the literature, our task goes by the name action segmentation [7, 8, 6, 15, 29, 16, 10] or action detection [28, 20, 21, 25].”
BackgroundMethodNeural Sign Language Translation
Camgöz, N. C., Hadfield, S., Koller, O., Ney, H., & Bowden, R. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00812 · Cited by 871
Human Action Recognition and Prediction: A Survey
Kong, Y., & Fu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01594-9 · Cited by 890
Singh, K. K., & Lee, Y. J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.381 · Cited by 724
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
Farha, Y. A., & Gall, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00369 · Cited by 673
“There is a huge line of research that addresses the action segmentation task in a weakly supervised setup [2, 10, 14, 21, 5].”
Background
Metadata
- Authors
- Huang, D.-A., Fei-Fei, L., & Niebles, J. C.
- Affiliations
- Stanford University (US)
- Corresponding author
- Huang, De-An
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 137-153
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2477205648
- Cited by
- 226
- References
- 50
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 15 · 2023: 22 · 2022: 32 · 2021: 38 · 2020: 33 · 2019: 28 · 2018: 26 · 2017: 16 · 2016: 3
- FWCI
- 8.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- video action recognition, connectionist temporal classification, dynamic programming, semi-supervised learning, temporal alignment
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