AlanaVLM: A Multimodal Embodied AI Foundation Model for Egocentric Video Understanding

Understand

AI personal assistants deployed via robots or wearables require embodied understanding to collaborate with humans effectively.

• However, current Vision-Language Models (VLMs) primarily focus on third-person view videos, neglecting the richness of egocentric perceptual experience.
• To address this gap, we propose three key contributions.
• First, we introduce the Egocentric Video Understanding Dataset (EVUD) for training VLMs on video captioning and question answering tasks specific to egocentric videos.