Summary of Alanavlm: a Multimodal Embodied Ai Foundation Model For Egocentric Video Understanding, by Alessandro Suglia et al.
AlanaVLM: A Multimodal Embodied AI Foundation Model for Egocentric Video Understanding
by Alessandro Suglia, Claudio Greco, Katie Baker, Jose L. Part, Ioannis Papaioannou, Arash Eshghi, Ioannis Konstas, Oliver Lemon
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Egocentric Video Understanding Dataset (EVUD) aims to address the gap in Vision-Language Models (VLMs) by focusing on egocentric perceptual experience. A 7B parameter VLM called AlanaVLM is trained using parameter-efficient methods and evaluated on OpenEQA, a challenging benchmark for embodied video question answering. The model achieves state-of-the-art performance, outperforming strong models including GPT-4-based planners by 3.6%. Additionally, it showcases competitive results compared to Gemini Pro Vision 1.0 and GPT-4V, even surpassing the latter in spatial reasoning. This research paves the way for building efficient VLMs that can be deployed in robots or wearables, leveraging embodied video understanding to collaborate seamlessly with humans in everyday tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI personal assistants need to understand our perspective to work well with us. Current models focus on videos from outside perspectives, but this paper proposes a new approach by introducing a dataset and model for understanding videos from the viewer’s point of view. The model is tested on challenging questions and achieves better results than other strong models. This research helps create AI assistants that can work effectively with humans in everyday tasks. |
Keywords
» Artificial intelligence » Gemini » Gpt » Parameter efficient » Question answering
