Published in Chinese Journal of Computers, 2024
The first Chinese survey on efficient transfer learning of large models.
Recommended citation: X. Li, J. Li, L. Zhu, and H. T. Shen. (2024). "Efficient transfer learning of large models with limited resources: A survey." Chinese Journal of Computers. 47(11).
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Published in Journal of University of Electronic Science and Technology of China, 2024
A review for cross-domain SOX estimation of lithium-ion batteries.
Recommended citation: X. Li, H. Chen, L. Shen, X. Feng, and J. Li. (2024). "Cross‑domain state estimation of lithium‑ion batteries: A review." Journal of University of Electronic Science and Technology of China. 53(5).
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Published in IEEE Transactions on Instrumentation and Measurement, 2022
Domain adaptative remaining useful life (RUL) prediction for machines.
Recommended citation: X. Li, J. Li, L. Zuo, L. Zhu, and H. T. Shen. (2022). "Domain adaptive remaining useful life prediction with transformer." IEEE Transactions on Instrumentation and Measurement.
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Published in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024
Multi-source-free domain adaptation (MSFDA) with high data and computational efficiency
Recommended citation: X. Li, J. Li, F. Li, L. Zhu, and K. Lu. (2024). "Agile multi‑source‑free domain adaptation." Proceedings of the AAAI Conference on Artificial Intelligence.
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Published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Unsupervised domain adaptation (UDA) of vision-language models (e.g., CLIP).
Recommended citation: Z. Du, X. Li, F. Li, K. Lu, L. Zhu, and J. Li. (2024). "Domain‑agnostic mutual prompting for unsupervised domain adaptation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
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Published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Unsupervised domain adaptation (UDA) of vision-language models (e.g., CLIP).
Recommended citation: X. Li, Y. Li, Z. Du, F. Li, K. Lu, and J. Li. (2024). "Split to merge: Unifying separated modalities for unsupervised domain adaptation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
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Published in Proceedings of the 30th ACM international conference on multimedia, 2022
Proposes and tackle a new transfer setting Source-Free-Active Domain Adaptation (SFADA).
Recommended citation: X. Li, Z. Du, J. Li, L. Zhu, and K. Lu. (2022). "Source‑free active domain adaptation via energy‑based locality preserving transfer." Proceedings of the 30th ACM international conference on multimedia.
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