Summary of Training a Vision Language Model As Smartphone Assistant, by Nicolai Dorka et al.
Training a Vision Language Model as Smartphone Assistant
by Nicolai Dorka, Janusz Marecki, Ammar Anwar
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
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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 paper presents a visual language model (VLM) that can control mobile devices by interacting with the user interface (UI). The VLM uses large language models (LLMs) to mimic human-like interactions, including gestures like tapping and swiping. This allows the agent to interact with any application on the device, making it suitable for executing a wide range of tasks. Unlike previous methods, our model operates on vision-language sentences created from sequences of past screenshots along with corresponding actions. The method is evaluated on the challenging Android in the Wild benchmark, demonstrating its promising efficacy and potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a smart digital assistant that can do many things for you, like control your mobile phone. It’s called a visual language model (VLM) and it works by looking at what’s on your screen and doing actions just like a human would. For example, it can tap or swipe to navigate through apps. This makes it really good at controlling any app on your device. The VLM is special because it looks at past screens and the actions that were taken to figure out how to do things. It’s tested on a hard task called Android in the Wild and shows great results. |
Keywords
» Artificial intelligence » Language model
