Lilang Lin
Orcid: 0000-0002-3229-4096
According to our database1,
Lilang Lin
authored at least 18 papers
between 2020 and 2025.
Collaborative distances:
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Bibliography
2025
Self-Supervised Skeleton Representation Learning Via Actionlet Contrast and Reconstruct.
IEEE Trans. Pattern Anal. Mach. Intell., November, 2025
2024
Mutual Information Driven Equivariant Contrastive Learning for 3D Action Representation Learning.
IEEE Trans. Image Process., 2024
IEEE Trans. Geosci. Remote. Sens., 2024
CoRR, 2024
Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
Proceedings of the Computer Vision - ECCV 2024, 2024
Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition.
Proceedings of the Computer Vision - ECCV 2024, 2024
2023
ACM Trans. Multim. Comput. Commun. Appl., 2023
Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning.
Proceedings of the 31st ACM International Conference on Multimedia, 2023
Bayesian Contrastive Learning with Manifold Regularization for Self-Supervised Skeleton Based Action Recognition.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2023
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
2022
IEEE Trans. Multim., 2022
S<sup>5</sup>Mars: Self-Supervised and Semi-Supervised Learning for Mars Segmentation.
CoRR, 2022
2021
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021
2020
MS<sup>2</sup>L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition.
CoRR, 2020
Proceedings of the MM '20: The 28th ACM International Conference on Multimedia, 2020