Still Image Action Recognition Using Ensemble Learning

In recent years, human action recognition in still images has become a challenge in computer vision. Most methods in this field use annotations such as human and object bounding boxes to determine human-object interaction and pose estimation. Preparing these annotations is time-consuming and costly. In this paper, an ensembling-based method is presented to avoid any additional annotations. According to this fact that a network performance on fewer classes of a dataset is often better than its performance on whole classes; the dataset is first divided into four groups. Then these groups are applied to train four lightweight Convolutional Neural Networks (CNNs). Consequently, each of these CNNs will specialize on a specific subset of the dataset. Then, the final convolutional feature maps of these networks are concatenated together. Moreover, a Feature Attention Module (FAM) is trained to identify the most important features among concatenated features for final prediction. The proposed method on the Stanford40 dataset achieves 86.86% MAP, which indicates this approach can obtain promising performance compared with many existing methods that use annotations.