GUI Action Narrator: Where and When Did That Action Take Place?

• URL: http://arxiv.org/abs/2406.13719v1
• Date: Wed, 19 Jun 2024 17:22:11 GMT
• Title: GUI Action Narrator: Where and When Did That Action Take Place?
• Authors: Qinchen Wu, Difei Gao, Kevin Qinghong Lin, Zhuoyu Wu, Xiangwu Guo, Peiran Li, Weichen Zhang, Hengxu Wang, Mike Zheng Shou,
• Abstract summary: We develop a video captioning benchmark for GUI actions, comprising 4,189 diverse video captioning samples. This task presents unique challenges compared to natural scene video captioning. We introduce our GUI action dataset textbfAct2Cap as well as a simple yet effective framework, textbfGUI Narrator, for GUI video captioning.
• Score: 19.344324166716245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of Multimodal LLMs has significantly enhanced image OCR recognition capabilities, making GUI automation a viable reality for increasing efficiency in digital tasks. One fundamental aspect of developing a GUI automation system is understanding primitive GUI actions. This comprehension is crucial as it enables agents to learn from user demonstrations, an essential element of automation. To rigorously evaluate such capabilities, we developed a video captioning benchmark for GUI actions, comprising 4,189 diverse video captioning samples. This task presents unique challenges compared to natural scene video captioning: 1) GUI screenshots typically contain denser information than natural scenes, and 2) events within GUIs are subtler and occur more rapidly, requiring precise attention to the appropriate time span and spatial region for accurate understanding. To address these challenges, we introduce our GUI action dataset \textbf{Act2Cap} as well as a simple yet effective framework, \textbf{GUI Narrator}, for GUI video captioning that utilizes the cursor as a visual prompt to enhance the interpretation of high-resolution screenshots. Specifically, a cursor detector is trained on our dataset, and a multimodal LLM model with mechanisms for selecting keyframes and key regions generates the captions. Experimental results indicate that even for today's most advanced multimodal models, such as GPT-4o, the task remains highly challenging. Additionally, our evaluations show that our strategy effectively enhances model performance, whether integrated into the fine-tuning of open-source models or employed as a prompting strategy in closed-source models.

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