We introduce the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign class examples to recognize the instances of unseen signs. To this end, we propose to utilize the readily available descriptions in sign language dictionaries as an intermediate-level semantic representation for knowledge transfer. We introduce a new benchmark dataset called ASL-Text that consists of 250 sign language classes and their accompanying textual descriptions. Compared to the ZSL datasets in other domains (such as object recognition), our dataset consists of limited number of training examples for a large number of classes, which imposes a significant challenge. We propose a framework that operates over the body and hand regions by means of 3D-CNNs, and models longer temporal relationships via bidirectional LSTMs. By leveraging the descriptive text embeddings along with these spatio-temporal representations within a zero-shot learning framework, we show that textual data can indeed be useful in uncovering sign languages. We anticipate that the introduced approach and the accompanying dataset will provide a basis for further exploration of this new zero-shot learning problem.

本研究介绍零样本手语识别（ZSSLR）问题，提出利用手语字典中的文本描述作为知识转移的中间语义表示，并使用ASL-Text数据集和3D-CNN、双向LSTM模型来解决该问题，表明文本数据可以帮助识别手语。

零样本手语识别：文字数据能否揭示手语？