Summary of Multi-frame, Lightweight & Efficient Vision-language Models For Question Answering in Autonomous Driving, by Akshay Gopalkrishnan et al.
Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving
by Akshay Gopalkrishnan, Ross Greer, Mohan Trivedi
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, the authors develop a novel approach to Vision-Language Models (VLMs) and Multi-Modal Language models (MMLMs) for autonomous driving. These models are designed to provide interpretable textual reasoning and responses using traffic scene images and other data modalities. The proposed model, EM-VLM4AD, is an efficient and lightweight multi-frame vision language model that performs Visual Question Answering for autonomous driving tasks. EM-VLM4AD requires significantly less memory and floating point operations compared to existing approaches while achieving higher performance on the DriveLM dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to create a more practical and efficient system for autonomous driving by using a smaller, yet powerful language model that can process traffic scene images quickly and accurately. The authors’ approach is designed to help ensure safe and reliable autonomous driving by providing real-time textual reasoning and responses. |
Keywords
» Artificial intelligence » Language model » Multi modal » Question answering
