利用带文本描述的人类指令改进零样本动作识别
Improving Zero-Shot Action Recognition using Human Instruction with Text Description
AI总结：
针对零样本动作识别性能不佳的问题，提出利用带文本描述的人类指令的改进框架，通过人工标注动作文本特征匹配视频类别，可结合其他模型并持续优化，在UCF101和HMDB51上取得最优准确率且提升其他模型精度。
摘要：
零样本动作识别指无需任何训练样本即可识别视频中的动作，鉴于其能节省人力成本和训练时间，目前正受到广泛关注。尽管如此，零样本学习的性能仍不尽如人意，这限制了它的实际应用。为解决该问题，本研究提出一个框架，利用带文本描述的人类指令来改进零样本动作识别。该框架需人工描述视频内容，这会产生一定人力成本，但在很多场景下这笔成本是值得的。我们为每个动作人工标注文本特征，特征可以是单词、短语或句子。随后通过计算视频与所有文本特征的匹配度，就能预测视频的类别。此外，所提模型还能与其他模型结合以提升准确率。不仅如此，我们的模型可通过重复输入人类指令持续优化以提升精度。在UCF101和HMDB51数据集上的实验结果表明，我们的模型取得了最优准确率，同时也提升了其他模型的准确率。
英文摘要：
Zero-shot action recognition, which recognizes actions in videos without having received any training examples, is gaining wide attention considering it can save labor costs and training time. Nevertheless, the performance of zero-shot learning is still unsatisfactory, which limits its practical application. To solve this problem, this study proposes a framework to improve zero-shot action recognition using human instructions with text descriptions. The proposed framework manually describes video contents, which incurs some labor costs; in many situations, the labor costs are worth it. We manually annotate text features for each action, which can be a word, phrase, or sentence. Then by computing the matching degrees between the video and all text features, we can predict the class of the video. Furthermore, the proposed model can also be combined with other models to improve its accuracy. In addition, our model can be continuously optimized to improve the accuracy by repeating human instructions. The results with UCF101 and HMDB51 showed that our model achieved the best accuracy and improved the accuracies of other models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
