erences
AI (2020)
J. AI
EasyOCR: ready-to-use ocr with 80+ supported languages.
Note: https://github.com/JaidedAI/EasyOCR
Cited by: Auxiliary Modules.
Bai et al. (2025)
S. Bai, Y. Cai, R. Chen, K. Chen, X. Chen, Z. Cheng, L. Deng, W. Ding, C. Gao, C. Ge, W. Ge, Z. Guo, Q. Huang, J. Huang, F. Huang, B. Hui, S. Jiang, Z. Li, M. Li, M. Li, K. Li, Z. Lin, J. Lin, X. Liu, J. Liu, C. Liu, Y. Liu, D. Liu, S. Liu, D. Lu, R. Luo, C. Lv, R. Men, L. Meng, X. Ren, X. Ren, S. Song, Y. Sun, J. Tang, J. Tu, J. Wan, P. Wang, P. Wang, Q. Wang, Y. Wang, T. Xie, Y. Xu, H. Xu, J. Xu, Z. Yang, M. Yang, J. Yang, A. Yang, B. Yu, F. Zhang, H. Zhang, X. Zhang, B. Zheng, H. Zhong, J. Zhou, F. Zhou, J. Zhou, Y. Zhu, and K. Zhu
Qwen3-vl technical report.
External Links: 2511.21631,
Link
Cited by: Models.
Banerjee and Lavie (2005)
S. Banerjee and A. Lavie
METEOR: an automatic metric for MT evaluation with improved correlation with human judgments.
In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, J. Goldstein, A. Lavie, C. Lin, and C. Voss (Eds.),
Ann Arbor, Michigan, pp. 65–72.
External Links: Link
Cited by: Performance Metrics.
Cagliero et al. (2025)
L. Cagliero, L. Vaiani, E. Pastor, A. Koudounas, E. Baralis, V. Mazzia, S. Pollastrini, T. Gueudre, M. Giollo, D. Amberti, and Y. Wu
Detecting and mitigating challenges in zero-shot video summarization with video LLMs.
In Findings of the Association for Computational Linguistics: ACL 2025, W. Che, J. Nabende, E. Shutova, and M. T. Pilehvar (Eds.),
Vienna, Austria, pp. 286–301.
External Links: Link,
Document,
ISBN 979-8-89176-256-5
Cited by: 4th item,
Video-to-text Summarization,
Datasets.
Castellano (2014)
B. Castellano
PySceneDetect: video scene cut detection and analysis tool.
Note: https://github.com/Breakthrough/PySceneDetectAccessed: 2025
Cited by: Auxiliary Modules.
Clark et al. (2026)
C. Clark, J. Zhang, Z. Ma, J. S. Park, M. Salehi, R. Tripathi, S. Lee, Z. Ren, C. D. Kim, Y. Yang, V. Shao, Y. Yang, W. Huang, Z. Gao, T. Anderson, J. Zhang, J. Jain, G. Stoica, W. Han, A. Farhadi, and R. Krishna
Molmo2: open weights and data for vision-language models with video understanding and grounding.
External Links: 2601.10611,
Link
Cited by: Models.
Gong et al. (2014)
B. Gong, W. Chao, K. Grauman, and F. Sha
Diverse sequential subset selection for supervised video summarization.
In Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger (Eds.),
Vol. 27, pp. .
External Links: Link
Cited by: Video-to-text Summarization.
Hendria (2025)
W. F. Hendria
MSVD-indonesian: a benchmark for multimodal video-text tasks in indonesian.
External Links: 2306.11341,
Link
Cited by: Datasets.
Honnibal et al. (2020)
M. Honnibal, I. Montani, S. Van Landeghem, and A. Boyd
spaCy: Industrial-strength Natural Language Processing in Python.
External Links: Document
Cited by: Auxiliary Modules.
Hua et al. (2025)
H. Hua, Y. Tang, C. Xu, and J. Luo
V2Xum-llm: cross-modal video summarization with temporal prompt instruction tuning.
In Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence,
AAAI’25/IAAI’25/EAAI’25.
External Links: ISBN 978-1-57735-897-8,
Link,
Document
Cited by: Video-to-text Summarization,
Peformance comparison (RQ1): Is RLM beneficial for LLM-based video-to-text summarization of long-form videos compared to non-recursive zero-shot baselines?,
Table 1.
Jocher and Qiu (2023)
G. Jocher and J. Qiu
Ultralytics YOLO.
Note: https://github.com/ultralytics/ultralyticsVersion 8.0
Cited by: Auxiliary Modules.
Lee et al. (2025)
M. J. Lee, D. Gong, and M. Cho
Video summarization with large language models.
In Proceedings of the Computer Vision and Pattern Recognition Conference,
pp. 18981–18991.
External Links: Link
Cited