Although few-shot action recognition based on metric learning paradigm has
achieved significant success, it fails to address the following issues: (1)
inadequate action relation modeling and underutilization of multi-modal
information; (2) challenges in handling video matching problems with different
lengths and speeds, and video matching problems with misalignment of video
sub-actions. To address these issues, we propose a Two-Stream Joint Matching
method based on contrastive learning (TSJM), which consists of two modules:
Multi-modal Contrastive Learning Module (MCL) and Joint Matching Module (JMM).
The objective of the MCL is to extensively investigate the inter-modal mutual
information relationships, thereby thoroughly extracting modal information to
enhance the modeling of action relationships. The JMM aims to simultaneously
address the aforementioned video matching problems. The effectiveness of the
proposed method is evaluated on two widely used few shot action recognition
datasets, namely, SSv2 and Kinetics. Comprehensive ablation experiments are
also conducted to substantiate the efficacy of our proposed approach.

基于对比学习的双流联合匹配 (TSJM) 方法在少样本动作识别中解决了动作关系建模、多模态信息利用不足、不同长度和速度的视频匹配问题，以及视频子动作不对齐的匹配问题。

基于对比学习的少样本动作识别的双流联合匹配方法

Two-stream joint matching method based on contrastive learning for  few-shot action recognition

We introduce RIANN (Ring Intersection Approximate Nearest Neighbor search),
an algorithm for matching patches of a video to a set of reference patches in
real-time. For each query, RIANN finds potential matches by intersecting rings
around key points in appearance space. Its search complexity is reversely
correlated to the amount of temporal change, making it a good fit for videos,
where typically most patches change slowly with time. Experiments show that
RIANN is up to two orders of magnitude faster than previous ANN methods, and is
the only solution that operates in real-time. We further demonstrate how RIANN
can be used for real-time video processing and provide examples for a range of
real-time video applications, including colorization, denoising, and several
artistic effects.

RIANN 是一种用于实时视频匹配的算法，具有近似最近邻搜索的特性，可以在外观空间的关键点周围的环交集中查找潜在匹配项，其搜索复杂度与时间变化量成反比例关系，证明其比以前的 ANN 方法快两个数量级，可以用于实时视频处理，并提供了许多实时视频应用的示例，例如上色，降噪和各种艺术效果。