# Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus

arXiv: https://arxiv.org/abs/2209.14927 · HTML: https://mlsanity.com/paper/2209.14927/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 28 of its 33 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://mlsanity.com/paper/2209.06794/) (2022)
- [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://mlsanity.com/paper/2208.10442/) (2022)
- [Mapping Natural Language Instructions to Mobile UI Action Sequences](https://mlsanity.com/paper/2005.03776/) (2020)
- [Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning](https://mlsanity.com/paper/2108.03353/) (2021)
- [Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements](https://mlsanity.com/paper/2010.04295/) (2020)
- [UIBert: Learning Generic Multimodal Representations for UI Understanding](https://mlsanity.com/paper/2107.13731/) (2021)
- [A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility](https://mlsanity.com/paper/2202.02312/) (2022)
- [Screen Recognition: Creating Accessibility Metadata for Mobile Applications from Pixels](https://mlsanity.com/paper/2101.04893/) (2021)
- [ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces](https://mlsanity.com/paper/2012.12350/) (2020)
- [Screen Parsing: Towards Reverse Engineering of UI Models from Screenshots](https://mlsanity.com/paper/2109.08763/) (2021)
- [Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale](https://mlsanity.com/paper/2201.04100/) (2022)
- [VUT: Versatile UI Transformer for Multi-Modal Multi-Task User Interface Modeling](https://mlsanity.com/paper/2112.05692/) (2021)
- [Modeling Mobile Interface Tappability Using Crowdsourcing and Deep Learning](https://mlsanity.com/paper/1902.11247/) (2019)
- [Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis](https://mlsanity.com/paper/2204.02448/) (2022)

## Built on by (most cited first, 30 of 62)

- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024)
- [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://mlsanity.com/paper/2210.03347/) (2022)
- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [PaLI-X: On Scaling up a Multilingual Vision and Language Model](https://mlsanity.com/paper/2305.18565/) (2023)
- [AutoDroid: LLM-powered Task Automation in Android](https://mlsanity.com/paper/2308.15272/) (2023)
- [Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs](https://mlsanity.com/paper/2404.05719/) (2024)
- [OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web](https://mlsanity.com/paper/2402.17553/) (2024)
- [Rich Human Feedback for Text-to-Image Generation](https://mlsanity.com/paper/2312.10240/) (2023)
- [GUI Agents with Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2411.04890/) (2024)
- [ScreenAI: A Vision-Language Model for UI and Infographics Understanding](https://mlsanity.com/paper/2402.04615/) (2024)
- [Image Captioners Are Scalable Vision Learners Too](https://mlsanity.com/paper/2306.07915/) (2023)
- [A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models](https://mlsanity.com/paper/2503.23350/) (2025)
- [GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents](https://mlsanity.com/paper/2505.15810/) (2025)
- [Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automation](https://mlsanity.com/paper/2312.03003/) (2023)
- [Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms](https://mlsanity.com/paper/2410.18967/) (2024)
- [Foundations and Recent Trends in Multimodal Mobile Agents: A Survey](https://mlsanity.com/paper/2411.02006/) (2024)
- [Dual-View Visual Contextualization for Web Navigation](https://mlsanity.com/paper/2402.04476/) (2024)
- [Falcon-UI: Understanding GUI Before Following User Instructions](https://mlsanity.com/paper/2412.09362/) (2024)
- [Reinforced UI Instruction Grounding: Towards a Generic UI Task Automation API](https://mlsanity.com/paper/2310.04716/) (2023)
- [Tur[k]ingBench: A Challenge Benchmark for Web Agents](https://mlsanity.com/paper/2403.11905/) (2024)
- [MobileWorldBench: Towards Semantic World Modeling For Mobile Agents](https://mlsanity.com/paper/2512.14014/) (2025)
- [ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations](https://mlsanity.com/paper/2310.04869/) (2023)
- [MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructions](https://mlsanity.com/paper/2502.16796/) (2025)
- [Improving Language Understanding from Screenshots](https://mlsanity.com/paper/2402.14073/) (2024)
- [UIClip: A Data-driven Model for Assessing User Interface Design](https://mlsanity.com/paper/2404.12500/) (2024)
- [TinyClick: Single-Turn Agent for Empowering GUI Automation](https://mlsanity.com/paper/2410.11871/) (2024)
- [MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation](https://mlsanity.com/paper/2410.13757/) (2024)
- [Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models without Fine-Tuning](https://mlsanity.com/paper/2412.10840/) (2024)
- [Enhancing Vision-Language Pre-training with Rich Supervisions](https://mlsanity.com/paper/2403.03346/) (2024)

## Abstract (arXiv)

Mobile UI understanding is important for enabling various interaction tasks such as UI automation and accessibility. Previous mobile UI modeling often depends on the view hierarchy information of a screen, which directly provides the structural data of the UI, with the hope to bypass challenging tasks of visual modeling from screen pixels. However, view hierarchies are not always available, and are often corrupted with missing object descriptions or misaligned structure information. As a result, despite the use of view hierarchies could offer short-term gains, it may ultimately hinder the applicability and performance of the model. In this paper, we propose Spotlight, a vision-only approach for mobile UI understanding. Specifically, we enhance a vision-language model that only takes the screenshot of the UI and a region of interest on the screen -- the focus -- as the input. This general architecture of Spotlight is easily scalable and capable of performing a range of UI modeling tasks. Our experiments show that our model establishes SoTA results on several representative UI tasks and outperforms previous methods that use both screenshots and view hierarchies as inputs. Furthermore, we explore multi-task learning and few-shot prompting capacities of the proposed models, demonstrating promising results in the multi-task learning direction.