VideoGUI: A Benchmark for GUI Automation from Instructional Videos
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the challenge of automating Graphical User Interface (GUI) tasks by leveraging instructional videos for guidance, focusing on complex and challenging GUI operations that require following detailed procedures demonstrated in videos . This problem is not entirely new, as previous efforts have been made in GUI automation evaluation and benchmarking model performances using screenshots or HTML codes . However, the paper introduces a novel approach by utilizing instructional videos with human demonstrations to tackle more advanced and visually-driven GUI tasks, emphasizing the importance of visual signals over text instructions in completing such operations .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate a scientific hypothesis related to GUI automation evaluation using instructional videos. The hypothesis focuses on developing a comprehensive evaluation framework for GUI tasks that require individuals to follow instructional videos to replicate complex operations and achieve specific goals. The study emphasizes the importance of visual-centric perception over textual understanding in identifying visual goals and transitions between states to address challenging GUI tasks .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "VideoGUI: A Benchmark for GUI Automation from Instructional Videos" introduces several innovative ideas, methods, and models in the field of GUI automation evaluation and benchmarking . Here are some key points from the paper:
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VideoGUI Benchmark: The paper introduces the VideoGUI benchmark, which differs from existing benchmarks by sourcing data from instructional videos with human demonstrations. It features 86 challenging full tasks averaging 22.7 actions and 463 subtasks. The benchmark offers comprehensive evaluation with hierarchical planning and action categories .
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Complex GUI Tasks: The focus of the paper is on complex and challenging GUI tasks that often require individuals to follow instructional videos to replicate long procedure operations and achieve goals. The proposed evaluation framework covers high-level task procedures, mid-level action decomposition, and atomic-level action execution. The approach emphasizes UI visual-centric perception over textual understanding .
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Multi-Modal Agents: The paper highlights the promising potential of Large Language Models (LLMs) beyond language modeling. It mentions notable advancements in models like Chain of Thought (CoT) and ReAct, making LLMs more competent for tasks such as GUI automation .
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Model Performance Evaluation: The paper evaluates different models like GPT-4o in various scenarios, such as full task execution and subtask competitions. It discusses the challenges faced by models in executing full tasks and the improvements observed when providing manual planning assistance. The study emphasizes the importance of enhancing planning capabilities for achieving efficient system performance .
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Simulator Experiments: The paper presents a Minimalist GUI Agent Framework consisting of a Parser, a Planner, and an Actor. It evaluates the behavior of the GPT-4o agent on popular software like Powerpoint in a simulator environment. The evaluation results show the challenges faced by the agent in executing full tasks and subtasks, highlighting the need for improved planning capabilities .
Overall, the paper proposes a novel benchmark, emphasizes the importance of visual-centric perception in GUI automation, explores the potential of LLMs, and evaluates model performance in executing complex GUI tasks through simulator experiments . The paper "VideoGUI: A Benchmark for GUI Automation from Instructional Videos" introduces several key characteristics and advantages compared to previous methods in GUI automation evaluation and benchmarking :
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Complex and Challenging Tasks: Unlike existing benchmarks that focus on simpler domains and tasks described with single text instructions, VideoGUI emphasizes complex and challenging GUI tasks that require individuals to follow instructional videos for long procedure operations. This approach addresses the real-world scenario where users encounter difficulties in performing novel and advanced tasks that rely more on visual signals than text instructions .
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Visual-Centric Perception: The evaluation framework developed in VideoGUI prioritizes UI visual-centric perception over textual understanding. It focuses on identifying visual goals and transitions between states, which present significant challenges in GUI automation. This emphasis on visual signals is crucial for tackling tasks that extend beyond basic operations .
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Multi-Modal Agents: The paper highlights the potential of Large Language Models (LLMs) like GPT-4o for GUI automation tasks. By leveraging advancements in models such as Chain of Thought (CoT) and ReAct, LLMs become more competent for GUI automation, showcasing improved performance in tasks like scrolling and element inference .
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Comprehensive Evaluation Framework: VideoGUI introduces a comprehensive evaluation framework that covers high-level task procedures, mid-level action decomposition, and atomic-level action execution. This detailed evaluation structure allows for a thorough assessment of model performances in executing complex GUI tasks, providing insights into planning capabilities and system efficiency .
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Simulator Experiments: The paper conducts simulator experiments using the GPT-4o agent on popular software like Powerpoint. The evaluation results reveal the challenges faced by the agent in executing full tasks and subtasks, emphasizing the need for improved planning capabilities to enhance system performance and achieve accurate success rates .
Overall, VideoGUI stands out for its focus on complex tasks, visual-centric perception, utilization of LLMs, comprehensive evaluation framework, and simulator experiments, offering a significant advancement in GUI automation evaluation and benchmarking compared to previous methods.
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research works exist in the field of GUI automation from instructional videos. Noteworthy researchers in this area include:
- Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang
- Difei Gao, Lei Ji, Luowei Zhou, Kevin Qinghong Lin, Joya Chen, Zihan Fan, and Mike Zheng Shou
- Gilles Baechler, Srinivas Sunkara, Maria Wang, Fedir Zubach, Hassan Mansoor, Vincent Etter, Victor C˘arbune, Jason Lin, Jindong Chen, and Abhanshu Sharma
- Xinyu Zhang, Mengxue Kang, Fei Wei, Shuang Xu, Yuhe Liu, and Lin Ma
- Zecheng He, Srinivas Sunkara, Xiaoxue Zang, Ying Xu, Lijuan Liu, Nevan Wichers, Gabriel Schubiner, Ruby Lee, and Jindong Chen
The key to the solution mentioned in the paper on GUI automation from instructional videos involves developing a comprehensive evaluation framework that covers high-level task procedures, mid-level action decomposition, and atomic-level action execution. This approach emphasizes UI visual-centric perception over textual understanding, focusing on identifying visual goals and transitions between states, which present significant challenges .
How were the experiments in the paper designed?
The experiments in the paper were designed to evaluate GUI automation using instructional videos. The experiments focused on complex and challenging GUI tasks that require following instructional videos to replicate lengthy procedures and achieve specific goals. The framework developed for evaluation covered high-level task procedures, mid-level action decomposition, and atomic-level action execution, emphasizing visual-centric perception over textual understanding . The experiments involved simulating real-world application scenarios using the best performing LLM, GPT-4o, to study its behavior on popular software like Powerpoint. The experiments utilized a Minimalist GUI Agent Framework consisting of a Parser, a Planner, and an Actor to conduct high-level planning, generate mid-level plans, and execute actions based on input queries from vision previews or text instructions . The evaluation settings included assessing the success rates of different models in executing full tasks and subtasks, highlighting the challenges faced by the models in completing tasks successfully and the impact of manual planning assistance on improving success rates .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is VideoGUI . The code for the project is open source, as indicated by the reference to the model versions and their corresponding links in the baseline details section .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper focuses on developing a comprehensive evaluation framework for GUI automation from instructional videos, emphasizing complex and challenging GUI tasks that require following instructional videos to replicate lengthy procedures and achieve goals . The study introduces a Minimalist GUI Agent Framework consisting of a Parser, a Planner, and an Actor, which is evaluated using a simulator environment with the best performing LLM GPT-4o on popular software like Powerpoint . The evaluation includes high-level task procedures, mid-level action decomposition, and atomic-level action execution, showcasing the model's performance across different levels of planning and execution .
Furthermore, the detailed evaluation on action executions and performance by task difficulty provides insights into the effectiveness of different methods and tools in executing GUI actions . The study also compares the performance of the GPT-4o agent in full task execution and subtask competitions, highlighting the challenges faced by the agent and the impact of manual planning assistance on success rates . These analyses demonstrate a thorough investigation into the capabilities and limitations of the models in handling GUI automation tasks based on instructional videos.
Overall, the experiments and results in the paper offer substantial evidence supporting the scientific hypotheses by providing a rigorous evaluation framework, detailed performance metrics, and insightful comparisons that shed light on the effectiveness of different models and approaches in GUI automation from instructional videos.
What are the contributions of this paper?
The paper "VideoGUI: A Benchmark for GUI Automation from Instructional Videos" makes several key contributions:
- It introduces a benchmark for GUI automation sourced from instructional videos with human demonstrations, featuring 86 challenging full tasks averaging 22.7 actions and 463 subtasks .
- The paper offers a comprehensive evaluation framework covering high-level task procedures, mid-level action decomposition, and atomic-level action execution, emphasizing UI visual-centric perception over textual understanding .
- It focuses on complex and challenging GUI tasks that often require individuals to follow instructional videos to replicate long procedure operations and achieve goals .
- The study highlights the importance of enhancing planning capabilities for achieving efficient systems with accurate success rates in GUI automation tasks .
- The paper evaluates model performance on full task execution and subtask competitions, showcasing the challenges faced by models in executing full tasks and the improvements seen with manual planning assistance .
- It presents a Minimalist GUI Agent Framework consisting of a Parser, a Planner, and an Actor for executing GUI tasks based on input queries, high-level planning, mid-level plans, and action sequences .
- The research explores the performance of different models like GPT-4o in simulator environments, highlighting the difficulties in executing full tasks and the impact of manual planning on success rates .
What work can be continued in depth?
To delve deeper into the field of GUI automation from instructional videos, further work can focus on enhancing the performance of models in executing complex and challenging GUI tasks that require following instructional videos . This can involve developing more advanced evaluation frameworks that cover high-level task procedures, mid-level action decomposition, and atomic-level action execution . Additionally, exploring the integration of multi-modal agents, such as leveraging advancements in LLMs beyond language modeling, can be a promising area for further research to improve the competency of models for GUI automation tasks .