Image Text Representation

Image-text representation research focuses on creating shared semantic spaces where images and text can be meaningfully compared and analyzed, enabling tasks like image retrieval, visual question answering, and multimodal classification. Current research emphasizes improving the efficiency and robustness of existing models like CLIP, often through techniques such as collaborative vision-text optimization, multi-level representation learning, and the use of auxiliary tasks during training. These advancements are significant because they enhance the interpretability and performance of vision-language models, leading to improved applications in diverse fields including e-commerce, healthcare, and social media analysis.