Zero Shot Transfer

Zero-shot transfer aims to enable machine learning models to perform tasks on unseen data or categories without any task-specific training. Current research focuses on leveraging pre-trained vision-language models like CLIP, along with techniques such as prompt engineering and various adaptation strategies (e.g., linear probes, hypernetworks), to achieve effective zero-shot transfer across diverse domains, including image classification, object detection, and even summarization. This capability is significant because it reduces the reliance on large labeled datasets for every new task, improving efficiency and potentially democratizing access to advanced AI applications. Furthermore, ongoing work explores improving robustness and addressing limitations such as modality gaps and biases inherent in pre-trained models.