A survey on pre-training and transfer learning for multimodal Vision-Language Models

In recent years, Vision-Language Models (VLMs) have emerged as a significant breakthrough in multimodal learning, demonstrating remarkable progress in tasks such as image-text alignment, image generation, and semantic reasoning. This paper systematically reviews current VLM pretraining methodologies, including contrastive learning and generative paradigms, while providing an in-depth analysis of efficient transfer learning strategies such as prompt tuning, LoRA, and adapter modules. Through representative models like CLIP, BLIP, and GIT, we examine their practical applications in visual grounding, image-text retrieval, visual question answering, affective computing, and embodied AI. Furthermore, we identify persistent challenges in fine-grained semantic modeling, cross-modal reasoning, and cross-lingual transfer. Finally, we envision future trends in unified architectures, multimodal reinforcement learning, and domain adaptation, aiming to provide systematic reference and technical insights for subsequent research.