Cite
DesmokeNet: A Two-Stage Smoke Removal Pipeline Based on Self-Attentive Feature Consensus and Multi-Level Contrastive Regularization.
MLA
Chen, Wei-Ting, et al. “DesmokeNet: A Two-Stage Smoke Removal Pipeline Based on Self-Attentive Feature Consensus and Multi-Level Contrastive Regularization.” IEEE Transactions on Circuits & Systems for Video Technology, vol. 32, no. 6, June 2022, pp. 3346–59. EBSCOhost, https://doi.org/10.1109/TCSVT.2021.3106198.
APA
Chen, W.-T., Lou, H.-L., Fang, H.-Y., Chen, I.-H., Chen, Y.-W., Ding, J.-J., & Kuo, S.-Y. (2022). DesmokeNet: A Two-Stage Smoke Removal Pipeline Based on Self-Attentive Feature Consensus and Multi-Level Contrastive Regularization. IEEE Transactions on Circuits & Systems for Video Technology, 32(6), 3346–3359. https://doi.org/10.1109/TCSVT.2021.3106198
Chicago
Chen, Wei-Ting, Hao-Lun Lou, Hao-Yu Fang, I-Hsiang Chen, Yi-Wen Chen, Jian-Jiun Ding, and Sy-Yen Kuo. 2022. “DesmokeNet: A Two-Stage Smoke Removal Pipeline Based on Self-Attentive Feature Consensus and Multi-Level Contrastive Regularization.” IEEE Transactions on Circuits & Systems for Video Technology 32 (6): 3346–59. doi:10.1109/TCSVT.2021.3106198.