We present a graph convolutional network with 2D pose estimation for the first time on child action recognition task achieving on par results with an RGB modality based model on a novel benchmark dataset containing unconstrained environment based videos.

This paper presents an implementation on child activity recognition (CAR) with a graph convolution network (GCN) based deep learning model since prior implementations in this domain have been dominated by CNN, LSTM and other methods despite the superior performance of GCN. To the best of our knowledge, we are the first to use a GCN model in child activity recognition domain. In overcoming the challenges of having small size publicly available child action datasets, several learning methods such as feature extraction, fine-tuning and curriculum learning were implemented to improve the model performance. Inspired by the contradicting claims made on the use of transfer learning in CAR, we conducted a detailed implementation and analysis on transfer learning together with a study on negative transfer learning effect on CAR as it hasn't been addressed previously. As the principal contribution, we were able to develop a ST-GCN based CAR model which, despite the small size of the dataset, obtained around 50% accuracy on vanilla implementations. With feature extraction and fine-tuning methods, accuracy was improved by 20%-30% with the highest accuracy being 82.24%. Furthermore, the results provided on activity datasets empirically demonstrate that with careful selection of pre-train model datasets through methods such as curriculum learning could enhance the accuracy levels. Finally, we provide preliminary evidence on possible frame rate effect on the accuracy of CAR models, a direction future research can explore.

相似性是一种比较主观度量，与所考虑的域中变化。在若干NLP应用程序中，例如文档分类，模式识别，聊天问题答案，情绪分析等，识别句子对的准确相似度得分已成为研究的关键领域。在评估相似性的现有模型中，基于上下文比较有效地计算这种相似性的限制，由于居中理论而定位，并且缺乏非语义文本比较已经证明是缺点。因此，本文介绍了基于网络科学，相邻加权关系边缘的原理的多层相似度测量的多层语义相似性网络模型，呈现了基于网络科学的原理和所提出的扩展节点相似性计算公式。评估和测试所提出的多层网络模型，并针对已建立的最先进模型进行测试，并且显示在评估句子相似性时表现出更好的性能分数。