CLIP Embeddings

CLIP embeddings, derived from the CLIP (Contrastive Language–Image Pre-training) model, represent a powerful technique for encoding both visual and textual information in a shared embedding space, enabling zero-shot transfer learning across various modalities. Current research focuses on mitigating biases inherent in CLIP embeddings, improving their quantitative accuracy (e.g., in counting objects), and leveraging them for enhanced performance in downstream tasks such as image generation, semantic segmentation, and object removal. This work is significant because it allows for more efficient and versatile multimodal applications, impacting fields ranging from computer vision and natural language processing to robotics and 3D scene understanding.