Comparative Analysis of Pretrained Models in Prefix-Based Image Captioning Tasks under Limited Training Conditions

Image captioning task requires effective combination of visual feature extraction and natural language generation. This study compares four pre-trained models such as Vision Transformer (ViT-B/16), ResNet-18, VGG-16, and DenseNet-121 when applied as frozen feature extractors in a prefix-based captioning framework using a partially trainable BERT-baseuncased text encoder. Experiments were conducted on a 32,000-image subset of the MS COCO 2017 captions dataset (28,000 training, 4,000 validation) under a limited training environment. Performance was evaluated using training cross-entropy loss. Results show DenseNet-121 achieved the lowest final loss (0.2894), followed by VGG-16 (0.3198), ResNet-18 (0.3935), and ViTB/16 (0.7002). DenseNet-121 demonstrated superior feature richness and fastest generalization, while ViT exhibited slowest convergence. These findings suggest that, under resource-constrained scenarios with frozen backbones, DenseNet-121 is the most effective choice among the evaluated architectures.