  Understanding the relationship between different parts of an image is crucial
in a variety of applications, including object recognition, scene
understanding, and image classification. Despite the fact that Convolutional
Neural Networks (CNNs) have demonstrated impressive results in classifying and
detecting objects, they lack the capability to extract the relationship between
different parts of an image, which is a crucial factor in Human Action
Recognition (HAR). To address this problem, this paper proposes a new module
that functions like a convolutional layer that uses Vision Transformer (ViT).
In the proposed model, the Vision Transformer can complement a convolutional
neural network in a variety of tasks by helping it to effectively extract the
relationship among various parts of an image. It is shown that the proposed
model, compared to a simple CNN, can extract meaningful parts of an image and
suppress the misleading parts. The proposed model has been evaluated on the
Stanford40 and PASCAL VOC 2012 action datasets and has achieved 95.5% mean
Average Precision (mAP) and 91.5% mAP results, respectively, which are
promising compared to other state-of-the-art methods.
