A proper semantic representation for encoding side information is key to the success of zero-shot learning. In this paper, we explore two alternative semantic representations especially for zero-shot human action recognition: textual descriptions of human actions and deep features extracted from still images relevant to human actions. Such side information are accessible on Web with little cost, which paves a new way in gaining side information for large-scale zero-shot human action recognition. We investigate different encoding methods to generate semantic representations for human actions from such side information. Based on our zero-shot visual recognition method, we conducted experiments on UCF101 and HMDB51 to evaluate two proposed semantic representations . The results suggest that our proposed text- and image-based semantic representations outperform traditional attributes and word vectors considerably for zero-shot human action recognition. In particular, the image-based semantic representations yield the favourable performance even though the representation is extracted from a small number of images per class.

探索了零样本人类动作识别的两种替代语义表示——人类动作的文本描述和从与人类动作相关的静止图像中提取的深度特征，结果表明我们提出的基于文本和图像的语义表示显著优于传统属性和向量模型，特别是图像语义表示，即使每个类别只是从少量图像中提取的。

零样本人体动作识别的替代语义表示