Language Image Pre Training

Language-image pre-training (LIP) aims to learn joint representations of images and their textual descriptions, enabling powerful zero-shot capabilities in various downstream tasks. Current research focuses on improving efficiency (e.g., through token pruning and merging, optimized loss functions like sigmoid loss), enhancing data utilization (e.g., using multi-perspective supervision and long captions), and addressing noisy or incomplete data. These advancements lead to more accurate and efficient models for applications such as image classification, retrieval, and semantic segmentation, impacting both computer vision and natural language processing research.