Comparison of Vision-Language Model (VLM) Pre-training Methods under Zero-Shot Image Classification Settings

Compares various vision-language model (VLM) pre-training approaches evaluated in zero-shot image classification settings. It highlights differences in training strategies, datasets, and performance, illustrating how pre-training objectives influence generalization to unseen image-text pairs.