Video sequences exhibit significant nuisance the-art results on NTU-60, NTU-120, Kinetics-skeleton and variations (undesired effects) of speed of actions, temporal UWA3D Multiview Activity II on supervised and unsupervised locations, and subjects' poses, leading to temporalviewpoint FSAR, and their meta-learning inspired fusion. Thus, we propose Joint tEmporal and cAmera viewpoiNt alIgnmEnt 1 Introduction (JEANIE) for sequence pairs. In particular, we focus on 3D skeleton sequences whose camera and subjects' poses can be Action recognition is a key topic in computer vision, easily manipulated in 3D. We evaluate JEANIE on skeletal with applications in video surveillance [105, 109, 120], Few-shot Action Recognition (FSAR), where matching well human-computer interaction, sport analysis and robotics. Given a query sequence, we create its several views labeling videos for 3D skeleton sequences is laborious, and by simulating several camera locations. For a support sequence, such pipelines need to be retrained or finetuned for new class we match it with view-simulated query sequences, concepts. Specifically, two-stream neural network [24, 23, 124] and 3D Convolutional each support temporal block can be matched to the Neural Network (3D CNN) [99, 9] aggregate framewise query temporal block with the same or adjacent (next) temporal and temporal block representations, respectively. However, index, and adjacent camera views to achieve joint local such networks are trained on large-scale datasets such temporal-viewpoint warping. JEANIE selects the smallest as Kinetics [9, 116, 110, 118] under a fixed set of training distance among matching paths with different temporalviewpoint classes. We also propose an Few-shot Learning (FSL) models for action recognition, unsupervised FSAR akin to clustering of sequences with termed Few-shot Action Recognition (FSAR), that rapidly JEANIE as a distance measure. JEANIE achieves state-of-adapt to novel classes given few training samples [77, 129, 31, 19, 138, 7, 112]. L. Wang is a Research Fellow at the School of Computing, the Australian J. Liu is an Assistant Professor at the Singapore University of Technology L. Zheng is an Associate Professor in the School of Computing, ANU.

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 show state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II.