The field of multimodal learning is rapidly advancing, with a focus on improving vision-language understanding. Recent developments have seen the introduction of new models and techniques that enable more effective integration of visual and textual information. One key direction is the use of large language models (LLMs) to enhance image analysis and interpretation. For example, LLMs are being used to automate the interpretation of non-destructive evaluation contour maps for bridge condition assessment, and to improve the analysis of infrared images. Another area of research is the development of multimodal datasets and benchmarks, such as MONITRS and RoadBench, which provide high-quality image-text pairs for training and evaluating multimodal models. These datasets are enabling the development of more accurate and robust models for tasks such as disaster response and road damage detection. Notable papers in this area include IRGPT, which introduces a bi-cross-modal curriculum transfer learning strategy for real-world infrared image analysis, and GRR-CoCa, which proposes an improved multimodal model architecture that leverages LLM mechanisms. Overall, the field is moving towards more effective and efficient integration of multimodal information, with potential applications in a wide range of areas, including infrastructure monitoring, disaster response, and scientific research.