Fetching the paper…
Reading the bibliography…
With the booming development of generative artificial intelligence (GAI), semantic communication (SemCom) has emerged as a new paradigm for reliable and efficient communication.
A. Hore and D. Ziou, “Image quality metrics: PSNR vs. SSIM,” in Proc. 20th Int. Conf. Pattern Recognit. (ICPR) , Aug. 2010, pp. 2366–2369
2010
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun, “Unified perceptual parsing for scene understanding,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 418–434
2018
Earlier work this paper cites.
R. Zhang et al. , “The unreasonable effectiveness of deep features as a perceptual metric,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2018, pp. 586–595
2018
Earlier work this paper cites.
C. H. Bahnsen and T. B. Moeslund, “Rain removal in traffic surveillance: Does it matter?” IEEE Trans. Intell. Transp. Syst. , vol. 20, no. 8, pp. 2802–2819, Oct. 2018
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) , vol. 1, no. 2, May. 2019
2019
Earlier work this paper cites.
H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Trans. Signal Process. , vol. 69, pp. 2663–2675, Apr. Apr. 2021
2021
Earlier work this paper cites.
Z. Weng and Z. Qin, “Semantic communication systems for speech transmission,” IEEE J. Sel. Areas Commun. , vol. 39, no. 8, pp. 2434–2444, Jun. Jun. 2021
2021
Earlier work this paper cites.
E. Xie et al. , “Segformer: Simple and efficient design for semantic segmentation with transformers,” Adv. Neural Inf. Process. Syst. , vol. 34, pp. 12 077–12 090, 2021
2021
Earlier work this paper cites.
A. Radford et al. , “Learning transferable visual models from natural language supervision,” in in Proc. 38th Int. Conf. Mach. Learn. (ICML) , May. 2021, pp. 8748–8763
2021
Earlier work this paper cites.
W. Yang et al. , “Semantic communications for future internet: Fundamentals, applications, and challenges,” IEEE Commun. Surveys Tuts. , vol. 25, no. 1, pp. 213–250, Jan. 2022
2022
Cited alongside, same era.
Y. Mao et al. , “Rate-splitting multiple access: Fundamentals, survey, and future research trends,” IEEE Commun. Surv. Tutor. , vol. 24, no. 4, pp. 2073–2126, July. 2022
2022
Cited alongside, same era.
Q. Pan et al. , “Image segmentation semantic communication over internet of vehicles,” in in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC) , Jul. 2023, pp. 1–6
2023
Cited alongside, same era.
Z. Yang, M. Chen, Z. Zhang, and C. Huang, “Energy efficient semantic communication over wireless networks with rate splitting,” IEEE J. Sel. Areas Commun. , vol. 41, no. 5, pp. 1484–1495, Jan. 2023
2023
Cited alongside, same era.
C. Zhao et al. , “Generative ai for secure physical layer communications: A survey,” IEEE Trans. Cogn. Commun. Netw. , Aug. 2024
2024
Later among the works it cites.
S. Tang, Q. Yang, D. Gündüz, and Z. Zhang, “Evolving semantic communication with generative modelling,” in in Proc. IEEE 35th Int. Symp. Personal, Indoor Mobile Radio Commun. (PIMRC) , Jan. Jan. 2024, pp. 1–6
2024
Later among the works it cites.
2024
Later among the works it cites.
G. Zhang, Q. Hu, Y. Cai, and G. Yu, “Scan: Semantic communication with adaptive channel feedback,” IEEE Trans. Cogn. Commun. Netw. , Apr. 2024
2024
Later among the works it cites.
Y. Liu et al. , “Select2col: Leveraging spatial-temporal importance of semantic information for efficient collaborative perception,” IEEE Veh. Technol. , Apr. 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
P. Yi, Y. Cao, X. Kang, and Y.-C. Liang, “Deep learning-empowered semantic communication systems with a shared knowledge base,” IEEE Trans. Wireless Commun. , vol. 23, no. 6, pp. 6174–6187, Nov. 2023
2023
Cited alongside, same era.
J. Achiam et al. , “Gpt-4 technical report,” 2023, arXiv:2303.08774
2023
Cited alongside, same era.
Y. Cheng et al. , “Resource allocation and common message selection for task-oriented semantic information transmission with rsma,” IEEE Trans. Wireless Commun. , Oct. 2023
2023
Cited alongside, same era.
W. X. Zhao et al. , “A survey of large language models,” 2023, arXiv:2303.18223
2023
Cited alongside, same era.
Y. Wen et al. , “Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery,” in Adv. Neural Inf. Process. Syst. , vol. 36, Dec. 2023, pp. 51 008–51 025
2023
Cited alongside, same era.
X. Liu et al. , “Instaflow: One step is enough for high-quality diffusion-based text-to-image generation,” in in Proc. 12th Int. Conf. Learn. Represent. (ICLR). , May. 2023
2023
Cited alongside, same era.
2024
Later among the works it cites.
S. Mahajan, T. Rahman, K. M. Yi, and L. Sigal, “Prompting hard or hardly prompting: Prompt inversion for text-to-image diffusion models,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2024, pp. 6808–6817
2024
Later among the works it cites.
2024
Later among the works it cites.
W. Yang et al. , “Rethinking generative semantic communication for multi-user systems with large language models,” IEEE Wireless Commun. , pp. 1–9, Apr. 2025
2025
Closest in time.
X. Han et al. , “Scsc: A novel standards-compatible semantic communication framework for image transmission,” IEEE Trans. Commun. , pp. 1–1, Jan. 2025
2025
Closest in time.
J. Lu et al. , “Generative artificial intelligence-enhanced multimodal semantic communication in internet of vehicles: System design and methodologies,” IEEE Veh. Technol. Mag. , vol. 20, no. 2, pp. 71–82, Mar. 2025
2025
Closest in time.