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-citation
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
Items 201–226 of 226
Continuous action recognition with weakly labelling videos
Lei, J. W., Li, G., Zhang, J., Li, S., & Wang, F. (2017) · Machine Vision Applications · doi:10.23919/mva.2017.7986846
Deep Learning for Action Understanding in Video
Shou, Z. (2019) · doi:10.7916/d8-srkz-t696
DDLSTM: Dual-Domain LSTM for Cross-Dataset Action RecognitionPreprint
Perrett, T., & Damen, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08634
Spatio-Temporal Event Segmentation and Localization for Wildlife Extended VideosPreprint
Mounir, R., Gula, R., Theuerkauf, J., & Sarkar, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02463 · Cited by 3
Filtjens, B., Ginis, P., Nieuwboer, A., Slaets, P., & Vanrumste, B. (2021) · arXiv (Cornell University)
Reconstructing and grounding narrated instructional videos in 3DPreprint
Zhukov, D., Rocco, I., Ivan Laptev, A., Šivic, J., Schönberger, J. L., Tekin, B., & Pollefeys, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04409
Notice of Retraction: Cascade Network with Boundary-Adaptive Filter for Surgical Gesture Recognition
Chen, H., Ni, Z.-L., Li, Z., & Bian, G. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533664
End-to-End Acoustic Model Using 1D CNN and BLSTM Networks with Focal CTC Loss
Fatehy, F. Z. E., Khalil, M., & Adib, A. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-90633-7_49
Review on Optimizing Text-Video Retrieval Using CLIP (Contrastive Language-Image Pre-training)
Sinha, S., Anupriya, A., & Chary, M. R. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-6383-4_27
United We Stand, Divided We Fall: UnityGraph for Unsupervised Procedure Learning from Videos
Bansal, S., Arora, C., & Jawahar, C. V. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00638
Lopera, J. C. L., Martínez, F. B., & Bocanegra, L. A. F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4820879
Xia, H., Zhan, Y., Liu, H., & Ren, X. (2024) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2300024
Deep Learning for Video Localization
Wu, Z., & Jiang, Y. (2024) · Wireless networks · doi:10.1007/978-3-031-57679-9_4
Fine-Grained Dynamic Network for Generic Event Boundary Detection
Zheng, Z., He, L., Yang, L., & Li, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72775-7_7 · Cited by 4
Gaze-Guided Task Decomposition for Imitation Learning in Robotic Manipulation
Takizawa, R., Ohmura, Y., & Kuniyoshi, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246512
Fine-grained Action Segmentation Network Based on Boundary Perception
Liu, Z., Chen, Y., Zhang, S., Tang, M., & Li, J. (2025) · International Conference on Intelligent Perception and Computer Vision (CIPCV) · doi:10.1109/cipcv65863.2025.00013
3D Symbolic Action Representation for Human Motion Understanding
Lu, Z., Liang, Y., Li, Q., & Lin, Y. (2026) · Neural Processing Letters · doi:10.1007/s11063-026-11835-4
Structured modeling and recognition of human actions in video
Chéron, G. (2018) · theses.fr (ABES) · doi:10.70675/12999662zc116z4dcazb558zc835f99061ee
Large-scale learning from video and natural language
Miech, A. (2020) · theses.fr (ABES) · doi:10.70675/e1c0d395ze48fz4d9bz821ezf11900bb536d
Spatial techniques for video understanding
Sydorov, V. (2021) · theses.fr (ABES) · doi:10.70675/ab9825adz1fb8z4f1cz9217zef67d5530022
Ye, W., Yang, X.-H., Ma, G.-F., Wei, D., Liu, S., & Feng, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461399
VS-DSN: Variable-Speed Dual-Stream Network for Continuous Sign Language Recognition
Zhou, G., Yang, B., Tang, W., & Yang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-22264-0_14
Structuring the Unstructured: A Zero-Shot Approach to Video Chaptering and Title Generation
Thakur, N., Paul, R., & Li, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31933-3_28
Weakly-supervised action segmentation with iterative soft boundary assignment
Ding, L., & Xu, C. (2018) · arXiv · doi:10.48550/arxiv.1803.10699 · Cited by 124
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
