Zero Shot Open-ended Video Inference
Ee Yeo Keat, Zhang Hao, Alexander Matyasko, Basura Fernando
Zero-shot open-ended inference on untrimmed videos poses a significant challenge, especially when no annotated data is utilized to navigate the inference direction. In this work, we aim to address this underexplored domain by introducing an adaptable framework that efficiently combines both the frozen vision-language (VL) model and off-the-shelf large language model (LLM) for conducting zero-shot open-ended inference tasks without requiring any additional training or fine-tuning. Our comprehensive experiments span various video action datasets for goal inference and action recognition tasks. The results demonstrate the framework's superior performance in goal inference compared to conventional vision-language models in open-ended and close-ended scenarios. Notably, the proposed framework exhibits the capability to generalize effectively to action recognition tasks, underscoring its versatility and potential contributions to advancing the video-based zero-shot understanding.