MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents

TL;DR

This paper introduces MA-EgoQA, a benchmark for understanding multiple egocentric videos from embodied agents, highlighting current challenges and proposing a baseline model to evaluate multi-agent video understanding capabilities.

Contribution

The paper formally defines a new problem of multi-egocentric video understanding, creates a benchmark dataset with questions, and proposes a baseline model for evaluating system-level comprehension.

Findings

Current models struggle with multi-stream egocentric video understanding.

MA-EgoQA contains 1.7k questions across five categories.

Baseline models show significant room for improvement.

Abstract

As embodied models become powerful, humans will collaborate with multiple embodied AI agents at their workplace or home in the future. To ensure better communication between human users and the multi-agent system, it is crucial to interpret incoming information from agents in parallel and refer to the appropriate context for each query. Existing challenges include effectively compressing and communicating high volumes of individual sensory inputs in the form of video and correctly aggregating multiple egocentric videos to construct system-level memory. In this work, we first formally define a novel problem of understanding multiple long-horizon egocentric videos simultaneously collected from embodied agents. To facilitate research in this direction, we introduce MultiAgent-EgoQA (MA-EgoQA), a benchmark designed to systemically evaluate existing models in our scenario. MA-EgoQA provides 1.7k questions unique to multiple egocentric streams, spanning five categories: social interaction, task coordination, theory-of-mind, temporal reasoning, and environmental interaction. We further propose a simple baseline model for MA-EgoQA named EgoMAS, which leverages shared memory across embodied agents and agent-wise dynamic retrieval. Through comprehensive evaluation across diverse baselines and EgoMAS on MA-EgoQA, we find that current approaches are unable to effectively handle multiple egocentric streams, highlighting the need for future advances in system-level understanding across the agents. The code and benchmark are available at https://ma-egoqa.github.io.