Multimodal CLIP

Multimodal CLIP leverages the power of combined image and text embeddings to perform various tasks, primarily focusing on improving zero-shot learning capabilities and enhancing model explainability. Current research explores CLIP's application in diverse areas, including multi-label classification, data filtering, and even generating videos guided by sound, often employing architectures like Vision Transformers and diffusion models to refine CLIP's inherent limitations. This work is significant because it addresses challenges in data bias, model interpretability, and cross-modal transfer learning, ultimately leading to more robust and reliable AI systems across various applications.