INTERNATIONAL FOCUS ON IMAGE DESCRIPTION - Durga, Ms T Usha (The journal of contemporary issues in business and government, 2023)
In recent years, the task of automatically generating image description has attracted a lot of attention in the field of artificial intelligence. Benefitting from the development of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), many approaches based on the CNN-RNN framework have been proposed to solve this task and achieved remarkable process. However, there remain two problems to be tackled in that most of existing methods only use imagelevel representation. One problem is object missing that there may miss some important objects when generating the image description and the other is misprediction that it may recognize one object to a wrong category. In this paper, to address the two problems, we propose a new method called global-local attention (GLA) for generating image description. The proposed GLA model utilizes attention mechanism to integrate objectlevel features with image- level feature. Through this manner, our model can selectively pay attention to objects and context information concurrently. Therefore, our proposed GLA method can generate more relevant image description sentences, and achieves the state-of-the-art performance on the well- known Microsoft COCO caption dataset with several popular evaluation metrics — CIDEr, METEOR, ROUGE-L and BLEU-1,2,3,4.