Fashion Image Retrieval

Fashion image retrieval focuses on efficiently searching and retrieving fashion items from large datasets using various query types, including images and text descriptions. Current research emphasizes multimodal approaches, integrating vision and language models (like Vision Transformers and Large Language Models) to handle complex queries and generate detailed descriptions, often incorporating techniques like self-supervised learning, dynamic feature fusion, and contrastive learning for improved accuracy and robustness. These advancements are significantly impacting e-commerce by enabling more personalized recommendations, improved virtual try-on experiences, and more effective search functionalities, ultimately enhancing the customer experience and driving industry efficiency.