# VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

arXiv: https://arxiv.org/abs/1908.04950 · HTML: https://mlsanity.com/paper/1908.04950/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [Episodic Memory Question Answering](https://mlsanity.com/paper/2205.01652/) (2022)
- [Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering](https://mlsanity.com/paper/2503.11117/) (2025)
- [Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications](https://mlsanity.com/paper/2302.00389/) (2023)
- [EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering](https://mlsanity.com/paper/2410.20263/) (2024)
- [Memory-Centric Embodied Question Answering](https://mlsanity.com/paper/2505.13948/) (2025)
- [CapNav: Benchmarking Vision Language Models on Capability-conditioned Indoor Navigation](https://mlsanity.com/paper/2602.18424/) (2026)
- [Multi-Step Reasoning for Embodied Question Answering via Tool Augmentation](https://mlsanity.com/paper/2510.20310/) (2025)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)
- [ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception](https://mlsanity.com/paper/2607.10180/) (2026)
- [A Data Source for Reasoning Embodied Agents](https://mlsanity.com/paper/2309.07974/) (2023)
- [Memory-Guided View Refinement for Dynamic Human-in-the-loop EQA](https://mlsanity.com/paper/2603.09541/) (2026)

## Abstract (arXiv)

Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.