Text to Image Retrieval

Text-to-image retrieval aims to find images semantically relevant to a given text query, a crucial task in multimedia search and analysis. Current research focuses on improving the accuracy and efficiency of retrieval, particularly by refining existing models like CLIP and exploring alternative architectures such as generative and transformer-based approaches that leverage both global and local image features, and address challenges like handling paraphrases and long-form text. These advancements are driving progress in various applications, including historical document analysis, medical image retrieval, and e-commerce, by enabling more effective and robust cross-modal search capabilities. The development of new benchmarks and datasets is also a key area, facilitating more rigorous evaluation and comparison of different methods.