论文标题
小组活动识别的细心合并
Attentive pooling for Group Activity Recognition
论文作者
论文摘要
在小组活动识别中，层次结构框架被广泛采用以表示个人及其相应小组之间的关系，并实现了有希望的绩效。但是，现有方法在此框架中仅采用了最大/平均池，这忽略了不同个体对小组活动识别的不同贡献。在本文中，我们提出了一种新的上下文合并方案，名为Ascentive Pooling，该方案可以从个人动作到小组活动的加权信息过渡。通过利用注意机制，细心的合并是可解释的，并且能够将成员环境嵌入到现有的层次模型中。为了验证拟议方案的有效性，设计了两种特定的专注合并方法，即全球专心池（GAP）和层次结构的稳定池（HAP）。差距奖励对小组活动意义重大的个体，而HAP通过引入亚组结构进一步考虑了层次结构。基准数据集上的实验结果表明，我们的建议在基线之外取得了显着优势，并且与最先进的方法相当。
In group activity recognition, hierarchical framework is widely adopted to represent the relationships between individuals and their corresponding group, and has achieved promising performance. However, the existing methods simply employed max/average pooling in this framework, which ignored the distinct contributions of different individuals to the group activity recognition. In this paper, we propose a new contextual pooling scheme, named attentive pooling, which enables the weighted information transition from individual actions to group activity. By utilizing the attention mechanism, the attentive pooling is intrinsically interpretable and able to embed member context into the existing hierarchical model. In order to verify the effectiveness of the proposed scheme, two specific attentive pooling methods, i.e., global attentive pooling (GAP) and hierarchical attentive pooling (HAP) are designed. GAP rewards the individuals that are significant to group activity, while HAP further considers the hierarchical division by introducing subgroup structure. The experimental results on the benchmark dataset demonstrate that our proposal is significantly superior beyond the baseline and is comparable to the state-of-the-art methods.
