Image Retrieval Task

Image retrieval aims to efficiently locate images within a large database that are visually similar to a given query, whether it's an image or text description. Current research emphasizes improving retrieval accuracy and efficiency through techniques like leveraging large language models (LLMs) and vision-language models (VLMs) for richer semantic understanding, incorporating user feedback for personalized results, and developing novel loss functions and training strategies to address challenges such as label noise and the semantic gap. These advancements have significant implications for various applications, including internet search, medical image analysis, and e-commerce, by enabling more accurate and relevant image searches.