| Challenge: | Existing models rely on UI metadata, which is often missing or not accessible. |
| Approach: | They propose a vision and language model that can learn generic visio-linguistic representations of UIs . they use a dataset to train Lexi, which is based on UI metadata . |
| Outcome: | The proposed model can handle unique features of UI screens, including text richness and context sensitivity. |
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| Challenge: | Prior work has learned strong visual representations with local or global captioning losses, but fails to retain both granularities. |
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Learning to Represent Image and Text with Denotation Graph (2020.emnlp-main)
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| Challenge: | Recent advances in learning representations of visual and language information have been a problem with many applications. |
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Mapping Natural Language Instructions to Mobile UI Action Sequences (2020.acl-main)
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| Challenge: | a new problem of grounding natural language instructions to mobile UI actions is emerging . we use a Transformer to extract action phrase tuples from long-range natural language instruction . |
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Towards Better Semantic Understanding of Mobile Interfaces (2022.coling-1)
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Srinivas Sunkara, Maria Wang, Lijuan Liu, Gilles Baechler, Yu-Chung Hsiao, Jindong Chen, Abhanshu Sharma, James W. W. Stout
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Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
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| Challenge: | Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. |
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From Perception to Reasoning: Enhancing Vision-Language Models for Mobile UI Understanding (2025.findings-acl)
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Settaluri Lakshmi Sravanthi, Ankit Mishra, Debjyoti Mondal, Subhadarshi Panda, Rituraj Singh, Pushpak Bhattacharyya
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Are Visual-Linguistic Models Commonsense Knowledge Bases? (2022.coling-1)
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| Challenge: | PTLMs are used to extract knowledge from text on demand. |
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Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
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| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
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lingvis.io - A Linguistic Visual Analytics Framework (P19-3)
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Mennatallah El-Assady, Wolfgang Jentner, Fabian Sperrle, Rita Sevastjanova, Annette Hautli-Janisz, Miriam Butt, Daniel Keim
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Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models (2024.emnlp-main)
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| Challenge: | PIXEL is a vision transformer that has been pre-trained on rendered text . however, it is not able to outperform monolingual subwords like BERT . |
| Approach: | They propose to use PIXEL as a vision transformer to train on rendered text to explore the gap between its visual and linguistic understanding. |
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