Zero-Shot Action Recognition with ChatGPT-Based Instruction

As deep learning continues to evolve, datasets are becoming increasingly larger, leading to higher costs for manual labeling. Zero-shot learning eliminates the need for substantial labor costs to label training datasets. It even allows the model to predict new classes with slight modifications. However, the accuracy of zero-shot learning still remains relatively low and makes practical applications challenging. Some recent research has tried to improve accuracy by manually annotating features, but this approach again requires labor-intensive input. To reduce these labor costs, we employ ChatGPT, which can generate features automatically without any manual involvement. Importantly, this approach maintains high accuracy levels, surpassing other automated methods.