The field of multimodal large language models is moving towards improving the alignment and integration of vision and language representations. Researchers are exploring new training strategies, architectures, and evaluation datasets to enhance the visual understanding capabilities of these models. A key direction is the development of more effective methods for incorporating prior knowledge into vision encoders and aligning multimodal representations. Another area of focus is the design of more efficient and adaptable models that can be applied to a wide range of tasks and datasets. Notable papers in this area include:

• Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models, which proposes a novel metric and training framework to quantify and improve the impact of vision encoder's prior knowledge on MLLM performance.
• LangBridge: Interpreting Image as a Combination of Language Embeddings, which introduces a novel adapter that enables pretraining-free adapter transfer across different LLMs while maintaining performance.
• CoMP: Continual Multimodal Pre-training for Vision Foundation Models, which achieves remarkable improvements in multimodal understanding and downstream tasks through a carefully designed multimodal pre-training pipeline.