CLIP Model

CLIP (Contrastive Language–Image Pre-training) models are powerful multimodal architectures designed to learn joint representations of images and text, enabling zero-shot and few-shot learning across various vision-language tasks. Current research focuses on mitigating biases, improving efficiency through parameter-efficient fine-tuning and adapter methods, enhancing interpretability, and addressing challenges in low-resource languages and long-tailed distributions. These advancements are significant because they improve the robustness, fairness, and applicability of CLIP models in diverse real-world applications, ranging from image retrieval and classification to robotics and medical image analysis.