Zero Shot Vision Language

Zero-shot vision-language models aim to enable computers to understand and reason about images and text without explicit training on specific tasks. Current research focuses on improving these models' performance by leveraging pre-trained unimodal (image-only or text-only) encoders, developing novel pre-training tasks like image-caption correction, and exploring multi-teacher distillation to combine the strengths of different architectures. This field is significant because it advances the development of more efficient and robust AI systems capable of handling diverse real-world scenarios, impacting applications ranging from image captioning and visual question answering to semantic segmentation and object detection.