Text Image Pair

Text-image pairs are a central focus in multimodal learning, aiming to bridge the gap between visual and textual information for improved understanding and generation. Current research emphasizes enhancing the semantic alignment between text and images, often leveraging large language models (LLMs) to improve the comprehension of complex prompts and generate more accurate and relevant images. This involves developing novel architectures like diffusion models and contrastive learning methods, as well as addressing challenges such as handling spurious images and ensuring model safety. The advancements in this area have significant implications for various applications, including image retrieval, text-to-image generation, and multimodal sentiment analysis.