Maintain a Better Balance between Performance and Cost for Image Captioning by a Size-Adjustable Convolutional Module

Image captioning is a challenging AI problem that connects computer vision and natural language processing. Many deep learning (DL) models have been proposed in the literature for solving this problem. So far, the primary concern of image captioning has been focused on increasing the accuracy of generating human-style sentences for describing given images. As a result, state-of-the-art (SOTA) models are often too expensive to be implemented in computationally weak devices. In contrast, the primary concern of this paper is to maintain a balance between performance and cost. For this purpose, we propose using a DL model pre-trained for object detection to encode the given image so that features of various objects can be extracted simultaneously. We also propose adding a size-adjustable convolutional module (SACM) before decoding the features into sentences. The experimental results show that the model with the properly adjusted SACM could reach a BLEU-1 score of 82.3 and a BLEU-4 score of 43.9 on the Flickr 8K dataset, and a BLEU-1 score of 83.1 and a BLEU-4 score of 44.3 on the MS COCO dataset. With the SACM, the number of parameters is decreased to 108M, which is about 1/4 of the original YOLOv3-LSTM model with 430M parameters. Specifically, compared with mPLUG with 510M parameters, which is one of the SOTA methods, the proposed method can achieve almost the same BLEU-4 scores, but the number of parameters is 78% less than the mPLUG.