REFERENCES
1. Antinoro Pizzuto, E. A. Review of "The hands are the head of the mouth - The mouth as articulator in Sign Languages" by Penny Boyes Braem and Rachel Sutton-Spence (eds.). Sign Lang. Linguist. 2003, 6, 284-89.
2. Ong, S. C.; Ranganath, S. Automatic sign language analysis: a survey and the future beyond lexical meaning. IEEE. Trans. Pattern. Anal. Mach. Intell. 2005, 27, 873-91.
3. Tang, S.; He, J.; Guo, D.; Wei, Y.; Li, F.; Hong, R. Sign-IDD: iconicity disentangled diffusion for sign language production. In Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25). 2025, pp. 7266-74.
4. Bragg, D.; Koller, O.; Bellard, M.; et al. Sign language recognition, generation, and translation: an interdisciplinary perspective. In Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility. New York, NY, USA; 2019, pp. 16-31.
5. Rastgoo, R.; Kiani, K.; Escalera, S. Sign language recognition: a deep survey. Expert. Syst. Appl. 2021, 164, 113794.
6. Sahoo, L. K.; Varadarajan, V. Deep learning for autonomous driving systems: technological innovations, strategic implementations, and business implications - a comprehensive review. Complex. Eng. Syst. 2025, 5, 2.
7. Wu, X.; Han, W.; Yang, H.; Han, H.; Qiao, J.; Peng, X. Robust multivariable tracking control for biological wastewater treatment process with external disturbances and uncertainties. Complex. Eng. Syst. 2025, 5, 12.
8. Freeman, W. T.; Roth, M. Orientation histograms for hand gesture recognition. In: International workshop on automatic face and gesture recognition. 1995, pp. 296-301. Available from: https://www.merl.com/publications/docs/TR94-03.pdf[Last accessed on 24 Apr 2026].
9. Sun, C.; Zhang, T.; Bao, B. K.; Xu, C.; Mei, T. Discriminative exemplar coding for sign language recognition with kinect. IEEE. Trans. Cybern. 2013, 43, 1418-28.
10. Hu, H.; Zhao, W.; Zhou, W.; Wang, Y.; Li, H. SignBERT: pre-training of hand-model-aware representation for sign language recognition. In Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 2021, pp. 11087-96.
11. Gan, S.; Yin, Y.; Jiang, Z.; Xia, K.; Xie, L.; Lu, S. Contrastive learning for sign language recognition and translation. In Proceedings of the International Joint Conference on Artificial Intelligence; 2023, pp. 763-72.
12. Ren, Y.; Li, H.; Li, Y.; et al. Multi-modal isolated sign language recognition based on self-paced learning. Expert. Syst. Appl. 2025, 291, 128340.
13. Gao, L.; Zhu, L.; Hu, L.; Shi, P.; Wan, L.; Feng, W. A structure-based disentangled network with contrastive regularization for sign language recognition. Expert. Syst. Appl. 2025, 271, 126623.
14. Lichtsteiner, P.; Posch, C.; Delbruck, T. A 128 X 128 120db 30mw asynchronous vision sensor that responds to relative intensity change. In Proceedings of 2006 IEEE International Solid State Circuits Conference - Digest of Technical Papers. 2006, pp. 2060-69.
15. Brandli, C.; Berner, R.; Yang, M.; Liu, S. C.; Delbruck, T. A 240×180 130 db 3 μs latency global shutter spatiotemporal vision sensor. IEEE. J. Solid. State. Circuits. 2014, 49, 2333-41.
16. Mitrokhin, A.; Fermuller, C.; Parameshwara, C.; Aloimonos, Y. Event-based moving object detection and tracking. In Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2018, pp. 1-9.
17. Zhang, J.; Yang, X.; Fu, Y.; Wei, X.; Yin, B.; Dong, B. Object tracking by jointly exploiting frame and event domain. In Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2021, pp. 13043-52.
18. Zhang, P., Yin, H., Wang, Z., et al. EvSign: sign language recognition and translation with streaming events. InProceedings of the Computer Vision - ECCV 2024: 18th European Conference; 2024, pp. 335-51.
19. Jiang, Y.; Wang, Y.; Li, S.; et al. EvCSLR: Event-guided continuous sign language recognition and benchmark. IEEE. Trans. Multimedia. 2025, 27, 1349-61.
20. Zhou, H.; Zhou, W.; Zhou, Y.; Li, H. Spatial-temporal multi-cue network for continuous sign language recognition. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). 2020, pp. 13009-16.
21. Cui, R.; Liu, H.; Zhang, C. A deep neural framework for continuous sign language recognition by iterative training. IEEE. Trans. Multimedia. 2019, 21, 1880-91.
22. Liu, T.; Tao, T.; Zhao, Y.; Zhu, J. A two-stream sign language recognition network based on keyframe extraction method. Expert. Syst. Appl. 2024, 253, 124268.
23. De Castro, G. Z.; Guerra, R. R.; Guimarães, F. G. Automatic translation of sign language with multi-stream 3D CNN and generation of artificial depth maps. Expert. Syst. Appl. 2023, 215, 119394.
24. Lee, C.; Ng, K. K.; Chen, C.; Lau, H.; Chung, S.; Tsoi, T. American sign language recognition and training method with recurrent neural network. Expert. Syst. Appl. 2021, 167, 114403.
25. Guo, L.; Xue, W.; Guo, Q.; et al. Distilling cross-temporal contexts for continuous sign language recognition. In Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023, pp. 10771-80.
26. Hu, L.; Gao, L.; Liu, Z.; Feng, W. Continuous sign language recognition with correlation network. In Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023, pp. 2529-39.
27. Lu, H.; Salah, A. A.; Poppe, R. TCNet: continuous sign language recognition from trajectories and correlated regions. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence; 2024, pp. 3891-99.
28. Zheng, J.; Wang, Y.; Tan, C.; et al. CVT-SLR: contrastive visual-textual transformation for sign language recognition with variational alignment. In Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023, pp. 23141-50.
29. Chen, Y.; Zuo, R.; Wei, F.; Wu, Y.; Liu S.; Mak, B. Two-stream network for sign language recognition and translation. arXiv 2022, 35, 17043-56.
30. Cihan Camgoz, N.; Koller, O.; Hadfield, S.; Bowden, R. Sign language transformers: joint end-to-end sign language recognition and translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020, pp. 10023-33.
31. Hao, A.; Min, Y.; Chen, X. Self-mutual distillation learning for continuous sign language recognition. In Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 2021, pp. 11303-12.
32. Zuo, R.; Mak, B. C2SLR: consistency-enhanced continuous sign language recognition. In Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2022, pp. 5131-40.
33. Gao, W.; Fang, G.; Zhao, D.; Chen, Y. A Chinese sign language recognition system based on SOFM/SRN/HMM. Pattern. Recogn. 2004, 37, 2389-402.
34. Koller, O.; Zargaran, S.; Ney, H. Re-sign: re-aligned end-to-end sequence modelling with deep recurrent CNN-HMMs. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017, pp. 4297-305.
35. Graves, A.; Fernandez, S.; Gomez, F.; Schmidhuber, J. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the 23rd international conference on Machine learning; 2006, pp. 369-76.
36. Koller, O.; Zargaran, S.; Ney, H.; Bowden, R. Deep sign: hybrid CNN-HMM for continuous sign language recognition. 2016, pp. 136.
37. Zhang, J.; Wang, Q.; Wang, Q. A sign language recognition framework based on cross-modal complementary information fusion. IEEE. Trans. Multimedia. 2024, 26, 8131-44.
38. Jiao, P.; Min, Y.; Li, Y.; Wang, X.; Lei, L.; Chen, X. CoSign: exploring co-occurrence signals in skeleton-based continuous sign language recognition. In Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 2023, pp. 20676-86.
39. Hu, L.; Gao, L.; Liu, Z.; Feng, W. Self-emphasizing network for continuous sign language recognition. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. 2023, pp. 854-62.
40. Duarte, A.; Palaskar, S.; Ventura, L.; et al. How2Sign: a large-scale multimodal dataset for continuous American sign language. In Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2021, pp. 2735-44.
41. Parelli, M.; Papadimitriou, K.; Potamianos, G.; Pavlakos, G.; Maragos, P. Spatio-temporal graph convolutional networks for continuous sign language recognition. In Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2022, pp. 8457-61.
42. Kumar, P.; Gauba, H.; Pratim Roy, P.; Prosad Dogra, D. A multimodal framework for sensor based sign language recognition. Neurocomputing 2017, 259, 21-38.
43. Oszust, M.; Krupski, J. Isolated sign language recognition with depth cameras. Procedia. Comp. Sci. 2021, 192, 2085-94.
44. Zheng, L.; Liang, B. Sign language recognition using depth images. In Proceedings of the 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV); 2016, pp. 1-6.
45. Jiang, Y.; Wang, Y.; Li, S.; Zhang, Y.; Zhao, M.; Gao, Y. Event-based low-illumination image enhancement. IEEE. Trans. Multimedia. 2024, 26, 1920-31.
46. Gao, Y.; Li, S.; Li, Y.; Guo, Y.; Dai, Q. SuperFast: 200× video frame interpolation via event camera. IEEE. Trans. Pattern. Anal. Mach. Intell. 2023, 45, 7764-80.
47. Rebecq, H.; Ranftl, R.; Koltun, V.; Scaramuzza, D. High speed and high dynamic range video with an event camera. IEEE. Trans. Pattern. Anal. Mach. Intell. 2019, 43, 1964-80.
48. Liu, S.; Alexandru, R.; Dragotti, P. L. Convolutional ISTA network with temporal consistency constraints for video reconstruction from event cameras. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 2022, pp. 1935-39.
49. Zhang, L.; Zhang, H.; Zhu, C.; Guo, S.; Chen, J.; Wang, L. Fine-grained video deblurring with event camera. In Proceedings of the 27th International Conference. Springer; 2021, pp. 352-64.
50. Chen, H.; Teng, M.; Shi, B.; Wang, Y.; Huang, T. A residual learning approach to deblur and generate high frame rate video with an event camera. IEEE. Trans. Multimedia. 2022, 25, 5826-39.
51. Li, J.; Li, J.; Zhu, L.; Xiang, X.; Huang, T.; Tian, Y. Asynchronous spatio-temporal memory network for continuous event-based object detection. IEEE. Trans. Image. Process. 2022, 31, 2975-87.
52. Shi, Q.; Ye, Z.; Wang, J.; Zhang, Y. QISampling: an effective sampling strategy for event-based sign language recognition. IEEE. Signal. Process. Lett. 2023, 30, 768-72.
53. Vasudevan, A.; Negri, P.; Di Ielsi, C.; Linares-Barranco, B.; Serrano-Gotarredona, T. SL-animals-DVS: event-driven sign language animals dataset. Pattern. Anal. Appl. 2022, 25, 505.
54. Vasudevan, A.; Negri, P.; Linares-Barranco, B.; Serrano-Gotarredona, T. Introduction and analysis of an event-based sign language dataset. In Proceedings of the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020); 2020, pp. 675-82.
55. Wang, Y., Zhang, X., Wang, Y., Wang, H., Huang, C., Shen, Y. Event-based american sign language recognition using dynamic vision sensor. In Proceedings of the Wireless Algorithms, Systems, and Applications: 16th International Conference. 2021, Proceedings, Part Ⅲ 16. Springer; 2021, pp. 3-10.
56. Feichtenhofer, C.; Fan, H.; Malik, J.; He, K. SlowFast networks for video recognition. In Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV); 2019, pp. 6201-10.
57. Camgoz, N. C.; Hadfield, S.; Koller, O.; Ney, H.; Bowden, R. Neural sign language translation. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2018, pp. 7784-93.
58. Hu, Y.; Liu, S.; Delbruck, T. v2e: from video frames to realistic DVS events. In Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2021, pp. 1312-21.
59. Min, Y.; Hao, A.; Chai, X.; Chen, X. Visual alignment constraint for continuous sign language recognition. In Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 2021, pp. 11542-51.
60. Hu, L., Gao, L., Liu, Z., Feng, W. Temporal lift pooling for continuous sign language recognition. In: Lecture Notes in Computer Science. Cham: Springer; 2022. pp. 511-27.
61. Alyami, S.; Luqman, H. Swin-MSTP: swin transformer with multi-scale temporal perception for continuous sign language recognition. Neurocomputing 2025, 617, 129015.
62. Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. Grad-CAM: visual explanations from deep networks via gradient-based localization. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV); 2017, pp. 618-26.
63. Gan, S.; Yin, Y.; Jiang, Z.; Wen, H.; Xie, L.; Lu, S. SignGraph: a sign sequence is worth graphs of nodes. In Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2024, pp. 13470-79.
64. Zuo, R.; Wei, F.; Mak, B. Towards online continuous sign language recognition and translation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing; 2024, pp. 11050-67.
