Ego4D Natural Language Query

Ego4D natural language query (NLQ) research focuses on accurately locating the time segment in long, first-person videos that answers a given natural language question. Current approaches employ transformer-based models, often incorporating multi-modal and multi-scale features to effectively fuse visual and textual information, and leverage techniques like contrastive learning and efficient clip selection to handle the computational challenges of long videos. This field is significant for advancing video understanding and has potential applications in areas such as augmented reality and robotics, enabling more intuitive and natural interaction with video data.

Papers