Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements

• URL: http://arxiv.org/abs/2010.04295v1
• Date: Thu, 8 Oct 2020 22:56:03 GMT
• Title: Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements
• Authors: Yang Li, Gang Li, Luheng He, Jingjie Zheng, Hong Li, Zhiwei Guan
• Abstract summary: We propose widget captioning, a novel task for automatically generating language descriptions for user interface elements. Our dataset contains 162,859 language phrases created by human workers for annotating 61,285 UI elements.
• Score: 17.383434668094075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language descriptions of user interface (UI) elements such as alternative text are crucial for accessibility and language-based interaction in general. Yet, these descriptions are constantly missing in mobile UIs. We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces. We collected a large-scale dataset for widget captioning with crowdsourcing. Our dataset contains 162,859 language phrases created by human workers for annotating 61,285 UI elements across 21,750 unique UI screens. We thoroughly analyze the dataset, and train and evaluate a set of deep model configurations to investigate how each feature modality as well as the choice of learning strategies impact the quality of predicted captions. The task formulation and the dataset as well as our benchmark models contribute a solid basis for this novel multimodal captioning task that connects language and user interfaces.

Related papers

• Tell Me What's Next: Textual Foresight for Generic UI Representations [65.10591722192609]
  We propose Textual Foresight, a novel pretraining objective for learning UI screen representations. Textual Foresight generates global text descriptions of future UI states given a current UI and local action taken. We train with our newly constructed mobile app dataset, OpenApp, which results in the first public dataset for app UI representation learning.
  arXiv  Detail & Related papers  (2024-06-12T02:43:19Z)
• COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training [119.03392147066093]
  Recent autoregressive vision-language models have excelled in few-shot text generation tasks but face challenges in alignment tasks. We introduce the contrastive loss into text generation models, partitioning the language model into dedicated unimodal text processing and adept multimodal data handling components. To bridge this gap, this work introduces VideoDatasetName, an inaugural interleaved video-text dataset featuring comprehensive captions.
  arXiv  Detail & Related papers  (2024-01-01T18:58:42Z)
• Interpreting User Requests in the Context of Natural Language Standing Instructions [89.12540932734476]
  We develop NLSI, a language-to-program dataset consisting of over 2.4K dialogues spanning 17 domains. A key challenge in NLSI is to identify which subset of the standing instructions is applicable to a given dialogue.
  arXiv  Detail & Related papers  (2023-11-16T11:19:26Z)
• ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations [13.939350184164017]
  Multimodal Vision-Language Models (VLMs) enable powerful applications from their fused understanding of images and language. We adapt a recipe for generating paired text-image training data for VLMs to the UI domain by combining existing pixel-based methods with a Large Language Model (LLM) We generate a dataset of 335K conversational examples paired with UIs that cover Q&A, UI descriptions, and planning, and use it to fine-tune a conversational VLM for UI tasks.
  arXiv  Detail & Related papers  (2023-10-07T16:32:34Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages [105.54207724678767]
  Data scarcity is a crucial issue for the development of highly multilingual NLP systems. We propose XTREME-UP, a benchmark defined by its focus on the scarce-data scenario rather than zero-shot. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies.
  arXiv  Detail & Related papers  (2023-05-19T18:00:03Z)
• OSIC: A New One-Stage Image Captioner Coined [38.46732302316068]
  We propose a novel One-Stage Image Captioner (OSIC) with dynamic multi-sight learning. To obtain rich features, we use the Swin Transformer to calculate multi-level features. To enhance the global modeling of encoder for caption, we propose a new dual-dimensional refining module.
  arXiv  Detail & Related papers  (2022-11-04T08:50:09Z)
• Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning [34.24671403624908]
  Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen. We present Screen2Words, a novel screen summarization approach that automatically encapsulates essential information of a UI screen into a coherent language phrase.
  arXiv  Detail & Related papers  (2021-08-07T03:01:23Z)
• UIBert: Learning Generic Multimodal Representations for UI Understanding [12.931540149350633]
  We introduce a transformer-based joint image-text model trained through novel pre-training tasks on large-scale unlabeled UI data. Our key intuition is that the heterogeneous features in a UI are self-aligned, i.e., the image and text features of UI components, are predictive of each other. We propose five pretraining tasks utilizing this self-alignment among different features of a UI component and across various components in the same UI. We evaluate our method on nine real-world downstream UI tasks where UIBert outperforms strong multimodal baselines by up to 9.26% accuracy.
  arXiv  Detail & Related papers  (2021-07-29T03:51:36Z)
• ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces [12.52699475631247]
  We introduce a new pre-trained UI representation model called ActionBert. Our methodology is designed to leverage visual, linguistic and domain-specific features in user interaction traces to pre-train generic feature representations of UIs and their components. Experiments show that the proposed ActionBert model outperforms multi-modal baselines across all downstream tasks by up to 15.5%.
  arXiv  Detail & Related papers  (2020-12-22T20:49:52Z)
• A Multi-Perspective Architecture for Semantic Code Search [58.73778219645548]
  We propose a novel multi-perspective cross-lingual neural framework for code--text matching. Our experiments on the CoNaLa dataset show that our proposed model yields better performance than previous approaches.
  arXiv  Detail & Related papers  (2020-05-06T04:46:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.