论文标题
零拍动识别的全球语义描述符
Global Semantic Descriptors for Zero-Shot Action Recognition
论文作者
论文摘要
零射击动作识别（ZSAR）方法的成功与用于转移知识的语义侧信息的性质本质上有关，尽管这一方面尚未在文献中进行研究。这项工作基于动作对象和动作描述性句子的关系介绍了一种新的ZSAR方法。我们证明，使用描述句子代表所有对象类都会在使用释义估计方法作为嵌入器时生成准确的对象相关估计。我们还展示了如何仅基于一组句子而没有硬人标签的一组句子来估计一组动作类别的概率。在我们的方法中，将这两个全局分类器的概率（即使用在整个视频中计算出的功能）的概率合并，从而产生了有效的转移知识模型进行动作分类。我们的结果在动力学400数据集中是最先进的，并且在ZSAR评估下对UCF-101具有竞争力。我们的代码可从https://github.com/valterlej/objsentzsar获得
The success of Zero-shot Action Recognition (ZSAR) methods is intrinsically related to the nature of semantic side information used to transfer knowledge, although this aspect has not been primarily investigated in the literature. This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences. We demonstrate that representing all object classes using descriptive sentences generates an accurate object-action affinity estimation when a paraphrase estimation method is used as an embedder. We also show how to estimate probabilities over the set of action classes based only on a set of sentences without hard human labeling. In our method, the probabilities from these two global classifiers (i.e., which use features computed over the entire video) are combined, producing an efficient transfer knowledge model for action classification. Our results are state-of-the-art in the Kinetics-400 dataset and are competitive on UCF-101 under the ZSAR evaluation. Our code is available at https://github.com/valterlej/objsentzsar
