Exploring DenseNet for Image Captioning
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Abstract
Captioning images is a complicated process in computer vision that necessitates a combination of visual comprehension and language processing. Image captioning is highly useful in various fields like accessibility, robotics, and autonomous systems as it generates text descriptions from images automatically. Lately, DenseNet-121, a densely connected convolutional neural network (CNN), has shown great performance in image classification and transfer learning tasks. This study explores using DenseNet-121 as a feature extraction backbone in an image captioning model. We assess how well it performs in relation to other CNN models like ResNet and VGG in terms of both caption quality and computational efficiency.
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