DreamStruct: Understanding Slides and User Interfaces via Synthetic Data Generation

Autor:	Peng, Yi-Hao, Huq, Faria, Jiang, Yue, Wu, Jason, Li, Amanda Xin Yue, Bigham, Jeffrey, Pavel, Amy
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Enabling machines to understand structured visuals like slides and user interfaces is essential for making them accessible to people with disabilities. However, achieving such understanding computationally has required manual data collection and annotation, which is time-consuming and labor-intensive. To overcome this challenge, we present a method to generate synthetic, structured visuals with target labels using code generation. Our method allows people to create datasets with built-in labels and train models with a small number of human-annotated examples. We demonstrate performance improvements in three tasks for understanding slides and UIs: recognizing visual elements, describing visual content, and classifying visual content types. Comment: ECCV 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.00201 Zobrazit plný text záznamu View this record from Arxiv