Video sequences exhibit significant nuisance variations (undesired effects)
of speed of actions, temporal locations, and subjects' poses, leading to
temporal-viewpoint misalignment when comparing two sets of frames or evaluating
the similarity of two sequences. Thus, we propose Joint tEmporal and cAmera
viewpoiNt alIgnmEnt (JEANIE) for sequence pairs. In particular, we focus on 3D
skeleton sequences whose camera and subjects' poses can be easily manipulated
in 3D. We evaluate JEANIE on skeletal Few-shot Action Recognition (FSAR), where
matching well temporal blocks (temporal chunks that make up a sequence) of
support-query sequence pairs (by factoring out nuisance variations) is
essential due to limited samples of novel classes. Given a query sequence, we
create its several views by simulating several camera locations. For a support
sequence, we match it with view-simulated query sequences, as in the popular
Dynamic Time Warping (DTW). Specifically, each support temporal block can be
matched to the query temporal block with the same or adjacent (next) temporal
index, and adjacent camera views to achieve joint local temporal-viewpoint
warping. JEANIE selects the smallest distance among matching paths with
different temporal-viewpoint warping patterns, an advantage over DTW which only
performs temporal alignment. We also propose an unsupervised FSAR akin to
clustering of sequences with JEANIE as a distance measure. JEANIE achieves
state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D
Multiview Activity II on supervised and unsupervised FSAR, and their
meta-learning inspired fusion.

基于关节时间和相机视角的对齐方法（JEANIE）用于骨骼序列对动作识别的支持查询序列进行时间视角对齐，实现了有监督和无监督助推学习融合，取得了最新的实验结果。