论文标题
重建Humpty Dumpty：开放设置动作识别的多功能图自动编码器
Reconstructing Humpty Dumpty: Multi-feature Graph Autoencoder for Open Set Action Recognition
论文作者
论文摘要
大多数动作识别数据集和算法都假设一个封闭的世界，其中所有测试样本都是已知类别的实例。在开放式问题中，可以从已知或未知类别中绘制测试样本。现有的开放集操作识别方法通常是基于扩展封闭的设置方法，通过添加分类分数或功能距离的事后分析，并且不会捕获所有视频剪辑元素之间的关系。我们的方法使用重建误差来确定视频的新颖性，因为未知类别很难放回原处，因此比已知类别的视频更高的重建错误。由于其重建能力，我们将解决开放式动作识别问题的解决方案称为“笨拙的笨蛋”。 Humpty Dumpty是一种基于图形的新型自动编码器，它说明了剪贴画之间的上下文和语义关系，以改善重建。更大的重建误差会导致不可能重建动作的可能性增加，即不能再次将笨拙的笨蛋恢复到一起，表明该动作从未见过，并且是新颖/未知的。在两个公开可用的动作识别数据集（包括HMDB-51和UCF-101）上进行了广泛的实验，显示了开放式操作识别的最新性能。
Most action recognition datasets and algorithms assume a closed world, where all test samples are instances of the known classes. In open set problems, test samples may be drawn from either known or unknown classes. Existing open set action recognition methods are typically based on extending closed set methods by adding post hoc analysis of classification scores or feature distances and do not capture the relations among all the video clip elements. Our approach uses the reconstruction error to determine the novelty of the video since unknown classes are harder to put back together and thus have a higher reconstruction error than videos from known classes. We refer to our solution to the open set action recognition problem as "Humpty Dumpty", due to its reconstruction abilities. Humpty Dumpty is a novel graph-based autoencoder that accounts for contextual and semantic relations among the clip pieces for improved reconstruction. A larger reconstruction error leads to an increased likelihood that the action can not be reconstructed, i.e., can not put Humpty Dumpty back together again, indicating that the action has never been seen before and is novel/unknown. Extensive experiments are performed on two publicly available action recognition datasets including HMDB-51 and UCF-101, showing the state-of-the-art performance for open set action recognition.
