Summary of Ui-jepa: Towards Active Perception Of User Intent Through Onscreen User Activity, by Yicheng Fu et al.
UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity
by Yicheng Fu, Raviteja Anantha, Prabal Vashisht, Jianpeng Cheng, Etai Littwin
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 framework, UI-JEPA, leverages masking strategies to learn abstract user interface (UI) embeddings from unlabeled data through self-supervised learning, combined with a large language model (LLM) decoder fine-tuned for user intent prediction. The framework addresses the challenges of developing lightweight models that can operate on-device and provide low latency or heightened privacy. Additionally, two new UI-grounded multimodal datasets, “Intent in the Wild” (IIW) and “Intent in the Tame” (IIT), are introduced for few-shot and zero-shot UI understanding tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UI-JEPA is a framework that helps computers understand what people want to do on their devices. It uses a special kind of learning called self-supervised learning, which doesn’t require much data or training. The framework also combines this learning with a language model to predict what users intend to do. This approach can help reduce the need for extensive computing power and provide faster results. |
Keywords
» Artificial intelligence » Decoder » Few shot » Language model » Large language model » Self supervised » Zero shot
