# Screen2Vec: Semantic Embedding of GUI Screens and GUI Components

arXiv: https://arxiv.org/abs/2101.11103 · HTML: https://mlsanity.com/paper/2101.11103/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2021.

## Built on by (most cited first, 30 of 41)

- [Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning](https://mlsanity.com/paper/2108.03353/) (2021)
- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [Enabling Conversational Interaction with Mobile UI using Large Language Models](https://mlsanity.com/paper/2209.08655/) (2022)
- [A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility](https://mlsanity.com/paper/2202.02312/) (2022)
- [Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automation](https://mlsanity.com/paper/2312.03003/) (2023)
- [Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents](https://mlsanity.com/paper/2407.00993/) (2024)
- [Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale](https://mlsanity.com/paper/2201.04100/) (2022)
- [Mobile App Tasks with Iterative Feedback (MoTIF): Addressing Task Feasibility in Interactive Visual Environments](https://mlsanity.com/paper/2104.08560/) (2021)
- [VUT: Versatile UI Transformer for Multi-Modal Multi-Task User Interface Modeling](https://mlsanity.com/paper/2112.05692/) (2021)
- [Towards Better Semantic Understanding of Mobile Interfaces](https://mlsanity.com/paper/2210.02663/) (2022)
- [MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructions](https://mlsanity.com/paper/2502.16796/) (2025)
- [UI Layout Generation with LLMs Guided by UI Grammar](https://mlsanity.com/paper/2310.15455/) (2023)
- [MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation](https://mlsanity.com/paper/2410.13757/) (2024)
- [Understanding Mobile GUI: from Pixel-Words to Screen-Sentences](https://mlsanity.com/paper/2105.11941/) (2021)
- [Automatic Macro Mining from Interaction Traces at Scale](https://mlsanity.com/paper/2310.07023/) (2023)
- [MP-GUI: Modality Perception with MLLMs for GUI Understanding](https://mlsanity.com/paper/2503.14021/) (2025)
- [Boosting GUI Prototyping with Diffusion Models](https://mlsanity.com/paper/2306.06233/) (2023)
- [Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis](https://mlsanity.com/paper/2204.02448/) (2022)
- [Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models](https://mlsanity.com/paper/2110.07775/) (2021)
- [Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces](https://mlsanity.com/paper/2404.13521/) (2024)
- [CHOP: Mobile Operating Assistant with Constrained High-frequency Optimized Subtask Planning](https://mlsanity.com/paper/2503.03743/) (2025)
- [DiLogics: Creating Web Automation Programs With Diverse Logics](https://mlsanity.com/paper/2308.05828/) (2023)
- [Tell Me What's Next: Textual Foresight for Generic UI Representations](https://mlsanity.com/paper/2406.07822/) (2024)
- [GUing: A Mobile GUI Search Engine using a Vision-Language Model](https://mlsanity.com/paper/2405.00145/) (2024)
- [UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity](https://mlsanity.com/paper/2409.04081/) (2024)
- [Computational Approaches for App-to-App Retrieval and Design Consistency Check](https://mlsanity.com/paper/2309.10328/) (2023)
- [MEGA-GUI: Multi-stage Enhanced Grounding Agents for GUI Elements](https://mlsanity.com/paper/2511.13087/) (2025)
- [GreaseVision: Rewriting the Rules of the Interface](https://mlsanity.com/paper/2204.03731/) (2022)
- [HaHeAE: Learning Generalisable Joint Representations of Human Hand and Head Movements in Extended Reality](https://mlsanity.com/paper/2410.16430/) (2024)

## Abstract (arXiv)

Representing the semantics of GUI screens and components is crucial to data-driven computational methods for modeling user-GUI interactions and mining GUI designs. Existing GUI semantic representations are limited to encoding either the textual content, the visual design and layout patterns, or the app contexts. Many representation techniques also require significant manual data annotation efforts. This paper presents Screen2Vec, a new self-supervised technique for generating representations in embedding vectors of GUI screens and components that encode all of the above GUI features without requiring manual annotation using the context of user interaction traces. Screen2Vec is inspired by the word embedding method Word2Vec, but uses a new two-layer pipeline informed by the structure of GUIs and interaction traces and incorporates screen- and app-specific metadata. Through several sample downstream tasks, we demonstrate Screen2Vec's key useful properties: representing between-screen similarity through nearest neighbors, composability, and capability to represent user tasks.