mlsanityData to 14 Oct 2026

3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Na"ive

arXiv 2112.12668 ↗First posted 23 Dec 2021cs.CV cs.HC cs.LGcited by 10

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Abstract (arXiv)

In this paper, we propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignment between query and support sequences of 3D body joints, we propose an advanced variant of Dynamic Time Warping which jointly models each smooth path between the query and support frames to achieve simultaneously the best alignment in the temporal and simulated camera viewpoint spaces for end-to-end learning under the limited few-shot training data. Sequences are encoded with a temporal block encoder based on Simple Spectral Graph Convolution, a lightweight linear Graph Neural Network backbone (we also include a setting with a transformer). Finally, we propose a similarity-based loss which encourages the alignment of sequences of the same class while preventing the alignment of unrelated sequences. We demonstrate state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 45 of its 109 references that are in the map. First posted on arXiv 23 Dec 2021.

Builds on

45 of 109 references.

References · 45

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33 more
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Citing papers per year: 2022: 3; 2023: 2; 2024: 3; 2025: 2.

10 papers.