引入高阶注意力机制的人体行为识别

现有的视频行为识别方法在特征提取过程中，存在忽略各个特征之间相互作用关系的问题，对近似动作的区分效果不理想。因此，提出引入高阶注意力机制的人体行为识别方法。在深度卷积神经网络中引入高阶注意力模块，通过注意力机制建模和利用复杂和高阶的统计信息，对训练过程中特征图各个部分的权重进行重新分配，从而关注局部细粒度信息，产生有区别性的关注建议，捕获行为之间的细微差异。在UCF101和HMDB51这两个人体行为数据集上的实验结果表明，与现有方法相比，识别率得到了一定的提升，验证了所提出方法的有效性和鲁棒性，提高了对近似行为的辨别能力。 

Human Behavior Recognition with High-Order Attention Mechanism

Because the existing video behavior recognition methods had the problem that ignore the interactional relationship among features in the process of feature extraction, the effect of distinguishing approximate actions was poor. Therefore, a human behavior recognition method with high-order attention mechanism was proposed. A high-order attention module was introduced to a deep convolutional neural network, which modeled and utilized the complex and high-order statistics information in attention mechanism. The goal of attention was to reallocate the weight of each part of the feature map in the process of training, so as to focus on the local fine-grained information, produce the discriminative attention proposals, and capture the subtle differences among behaviors. Extensive experiments had been conducted to validate the superiority of our method for action recognition on two human behavior datasets, including UCF101 and HMDB51. The results showed that the recognition rate has been improved to a certain extent compare with the existing methods, which validated the effectiveness and robustness of the proposed method. The method effectively improves the ability to distinguish approximate behaviors.