Image Retrieval

Image retrieval focuses on efficiently finding images within large datasets that match a given query, whether that query is an image, text description, or a combination of both. Current research emphasizes improving retrieval accuracy and efficiency through various techniques, including contrastive learning, the adaptation of large language and vision-language models (like CLIP), and the development of novel architectures such as those incorporating attention mechanisms and hybrid convolutional-Transformer networks. These advancements have significant implications for diverse applications, ranging from digital humanities research to medical diagnosis and robotics, by enabling faster and more accurate searches across vast multimedia collections.