Text Feature

Text feature research focuses on effectively representing and integrating textual information with visual data, primarily aiming to improve the understanding and interaction between modalities. Current research emphasizes collaborative vision-text optimization within models like CLIP and Vision Transformers (ViTs), exploring techniques such as contrastive learning, hyperbolic embeddings, and prompt engineering to enhance alignment and robustness across diverse tasks. This work is significant for advancing applications like open-vocabulary segmentation, referring image segmentation, and text-guided image generation, impacting fields ranging from medical image analysis to remote sensing and person search.