Unseen Action

Unseen action recognition focuses on enabling computer systems to identify and understand actions not explicitly encountered during training, a crucial step towards robust artificial intelligence. Current research emphasizes leveraging large language models and vision-language models, often incorporating transformer architectures and generative adversarial networks, to bridge the semantic gap between seen and unseen actions through techniques like prompt engineering, knowledge transfer, and distribution matching. This field is vital for advancing applications such as robotics, video understanding, and cybersecurity, where systems must generalize to novel situations and adapt to unpredictable inputs.