Sign language recognition from sequences of monocular images or 2D poses is a
challenging field, not only due to the difficulty to infer 3D information from
2D data, but also due to the temporal relationship between the sequences of
information. Additionally, the wide variety of signs and the constant need to
add new ones on production environments makes it infeasible to use traditional
classification techniques. We propose a novel Contrastive Transformer-based
model, which demonstrate to learn rich representations from body key points
sequences, allowing better comparison between vector embedding. This allows us
to apply these techniques to perform one-shot or few-shot tasks, such as
classification and translation. The experiments showed that the model could
generalize well and achieved competitive results for sign classes never seen in
the training process.

本研究提出了一种新的基于对比学习的变形注意力模型，该模型能够从身体关键点序列中学习丰富的表示，用于手语分类和翻译任务，并通过实验证明其可以推而广之。