Aug 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 36 references
TL;DR
This work presents a novel self-supervised architecture centered on graph prototype learning that sets a new state-of-the-art on the ARMM dataset with an accuracy of 95.70%, substantiating the efficacy and transferability of prototype-guided self-supervised learning for skeleton-based action representation.
Auxiliary visual and language modalities can improve self-supervised skeleton action representation learning by supplying object, scene, and semantic cues that joint coordinates lack. Existing cross-modal training signals are often defined at the sample level or aligned in a single global space, making them sensitive t...
Kenan Ye, Shengjie Zhao, Shuang Liang· IEEE International Conferenc...· 0 citations
This work proposes MEMC (Masked Modeling with Efficient and Minimal Contrastive Learning), a novel framework that adopts an efficient sequential cascade strategy based on layer-grafted pretraining and introduces two CL enhancements to improve the discriminative capability of the learned representations.
Results show that prototype-mediated vision-language transfer can improve skeleton representations for the behavior-interpretation stage of positioning and tracking pipelines, and CrossVLS is proposed, which is a cross-modal vision-language-guided framework that transfers semantic knowledge from red, green, and blue fr...
Kenan Ye, Shengjie Zhao, Shuang Liang· Electronics· 0 citations
FineX is introduced, which factorizes fine-grained cues into RGB appearance, pose heatmap geometry, and skeletal-graph topology and raises mean class accuracy on Gym99, Gym288, and Diving48 without textual supervision or large-scale vision-language pre-training.
Imtiaz Ul Hassan, Tasweer Ahmad, Nik Bessis et al.· 0 citations
HPCLR is a hierarchical part-aware contrastive learning framework for skeleton-based action recognition that leverages the consistency among joint, motion, and bone modalities to select more reliable positive samples, thereby contributing to more stable and informative multi-stream skeleton representations.
Hong-Wei Chen, Min Wei, Yuanyuan Zhu et al.· Journal of Supercomputing· 0 citations
This proposed TDSM-MM has been ablated via extensive experiments and achieved the best inductive accuracy on three of four NTU-60/120 splits and surpasses the transductive state-of-the-art on NTU-120 96/24, suggesting that diffusion-based methods can be a promising direction for zero-shot learning.
Zehao Bao, Shu-Jun Guo, Bruce X. B. Yu· 0 citations
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