Authors:
(1) An Yan, UC San Diego, ayan@ucsd.edu;
(2) Zhengyuan Yang, Microsoft Corporation, zhengyang@microsoft.com with equal contributions;
(3) Wanrong Zhu, UC Santa Barbara, wanrongzhu@ucsb.edu;
(4) Kevin Lin, Microsoft Corporation, keli@microsoft.com;
(5) Linjie Li, Microsoft Corporation, lindsey.li@mocrosoft.com;
(6) Jianfeng Wang, Microsoft Corporation, jianfw@mocrosoft.com;
(7) Jianwei Yang, Microsoft Corporation, jianwei.yang@mocrosoft.com;
(8) Yiwu Zhong, University of Wisconsin-Madison, yzhong52@wisc.edu;
(9) Julian McAuley, UC San Diego, jmcauley@ucsd.edu;
(10) Jianfeng Gao, Microsoft Corporation, jfgao@mocrosoft.com;
(11) Zicheng Liu, Microsoft Corporation, zliu@mocrosoft.com;
(12) Lijuan Wang, Microsoft Corporation, lijuanw@mocrosoft.com. Editor’s note: This is the part 7 of 13 of a paper evaluating the use of a generative AI to navigate smartphones. You can read the rest of the paper via the table of links below. Table of Links Abstract and 1 Introduction
2 Related Work
3 MM-Navigator
3.1 Problem Formulation and 3.2 Screen Grounding and Navigation via Set of Mark
3.3 History Generation via Multimodal Self Summarization
4 iOS Screen Navigation Experiment
4.1 Experimental Setup
4.2 Intended Action Description
4.3 Localized Action Execution and 4.4 The Current State with GPT-4V
5 Android Screen Navigation Experiment
5.1 Experimental Setup
5.2 Performance Comparison
5.3 Ablation Studies
5.4 Error Analysis
6 Discussion
7 Conclusion and References 4.3 Localized Action Execution A natural question is how reliable GPT-4V can convert its understanding of the screen into executable actions. Table 1 shows an accuracy of 74.5% on selecting the location that could lead to the desired outcome. Figure 2 shows the added marks with interactive SAM (Yang et al., 2023b; Kirillov et al., 2023), and the corresponding GPT-4V outputs. As shown in Figure 2(a), GPT-4V can select the “X” symbol (ID: 9) to close the tabs, echoing its previous description in Figure 1(a). GPT-4V is also capable of selecting the correct location to click from the large portion of clickable icons, such as the screen shown in (b). Figure 1(c) represents a complicated screen with various images and icons, where GPT-4V can select the correct mark 8 for the reading the “6 Alerts.” Within a screen with various texts, such as (d), GPT-4V can identify the clickable web links, and locate the queried one with the correct position 18. 4.4 The Current State with GPT-4V From the analytical experiments on iOS screens, we find GPT-4V is capable of performing GUI navigation. Although several types of failure cases still occur, as outlined below, MM-Navigator shows promise for executing multi-screen navigation to fulfill real-world smartphone use cases. We conclude the section with qualitative results on such episode-level navigation queries. Failure cases. Despite the promising results, GPT-4V does make errors in the zero-shot screen navigation task, as shown in Table 1. These errors are illustrated through representative failure cases as follows. (a) GPT-4V might not generate the correct answer in a single step when the query involves knowledge the model lacks. For example, GPT-4V is not aware that only “GPT-4” can support image uploads, hence it fails to click the “GPT-4” icon before attempting to find the image uploading function. (b) Although usually reliable, GPT-4V might still select the incorrect location. An example of this is selecting the mark 15 for the “ChatGPT” app instead of the correct mark 5. (c) In complex scenarios, GPT-4V’s initial guess might not be correct, such as clicking the “numeric ID 21 for the 12-Hour Forecast” instead of the correct answer of mark 19. (d) When the correct clickable area is not marked, like a “+” icon without any marks, GPT-4V cannot identify the correct location and may reference an incorrect mark instead. Finally, we note that many of those single-step failures may be corrected with iterative explorations, leading to the correct episode-level outcome. From single screens to complete episodes. MMNavigator shows an impressive capability in performing GUI navigation in a zero-shot manner. We further extend MM-Navigator from processing a single cellphone screen to recursively processing an episode of screen inputs. Figure 4 shows the qualitative result. In each step, we include the objective, “You are asked to shop for a milk frother, your budget is between $50 and $100.” and its previous action in the prompt to GPT-4V. We show that the model can effectively perform multi-step reasoning to accomplish the given shopping instruction. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) An Yan, UC San Diego, ayan@ucsd.edu; (2) Zhengyuan Yang, Microsoft Corporation, zhengyang@microsoft.com with equal contributions; (3) Wanrong Zhu, UC Santa Barbara, wanrongzhu@ucsb.edu; (4) Kevin Lin, Microsoft Corporation, keli@microsoft.com; (5) Linjie Li, Microsoft Corporation, lindsey.li@mocrosoft.com; (6) Jianfeng Wang, Microsoft Corporation, jianfw@mocrosoft.com; (7) Jianwei Yang, Microsoft Corporation, jianwei.yang@mocrosoft.com; (8) Yiwu Zhong, University of Wisconsin-Madison, yzhong52@wisc.edu; (9) Julian McAuley, UC San Diego, jmcauley@ucsd.edu; (10) Jianfeng Gao, Microsoft Corporation, jfgao@mocrosoft.com; (11) Zicheng Liu, Microsoft Corporation, zliu@mocrosoft.com; (12) Lijuan Wang, Microsoft Corporation, lijuanw@mocrosoft.com. Authors: Authors: (1) An Yan, UC San Diego, ayan@ucsd.edu; (2) Zhengyuan Yang, Microsoft Corporation, zhengyang@microsoft.com with equal contributions; (3) Wanrong Zhu, UC Santa Barbara, wanrongzhu@ucsb.edu; (4) Kevin Lin, Microsoft Corporation, keli@microsoft.com; (5) Linjie Li, Microsoft Corporation, lindsey.li@mocrosoft.com; (6) Jianfeng Wang, Microsoft Corporation, jianfw@mocrosoft.com; (7) Jianwei Yang, Microsoft Corporation, jianwei.yang@mocrosoft.com; (8) Yiwu Zhong, University of Wisconsin-Madison, yzhong52@wisc.edu; (9) Julian McAuley, UC San Diego, jmcauley@ucsd.edu; (10) Jianfeng Gao, Microsoft Corporation, jfgao@mocrosoft.com; (11) Zicheng Liu, Microsoft Corporation, zliu@mocrosoft.com; (12) Lijuan Wang, Microsoft Corporation, lijuanw@mocrosoft.com. Editor’s note: This is the part 7 of 13 of a paper evaluating the use of a generative AI to navigate smartphones. You can read the rest of the paper via the table of links below. Editor’s note: This is the part 7 of 13 of a paper evaluating the use of a generative AI to navigate smartphones. You can read the rest of the paper via the table of links below. Editor’s note: This is the part 7 of 13 of a paper evaluating the use of a generative AI to navigate smartphones. You can read the rest of the paper via the table of links below. Editor’s note: This is the part 7 of 13 of a paper evaluating the use of a generative AI to navigate smartphones. You can read the rest of the paper via the table of links below. Table of Links Abstract and 1 Introduction 2 Related Work 3 MM-Navigator 3.1 Problem Formulation and 3.2 Screen Grounding and Navigation via Set of Mark 3.3 History Generation via Multimodal Self Summarization 4 iOS Screen Navigation Experiment 4.1 Experimental Setup 4.2 Intended Action Description 4.3 Localized Action Execution and 4.4 The Current State with GPT-4V 5 Android Screen Navigation Experiment 5.1 Experimental Setup 5.2 Performance Comparison 5.3 Ablation Studies 5.4 Error Analysis 6 Discussion 7 Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 MM-Navigator 3.1 Problem Formulation and 3.2 Screen Grounding and Navigation via Set of Mark 3.1 Problem Formulation and 3.2 Screen Grounding and Navigation via Set of Mark 3.3 History Generation via Multimodal Self Summarization 3.3 History Generation via Multimodal Self Summarization 4 iOS Screen Navigation Experiment 4.1 Experimental Setup 4.1 Experimental Setup 4.2 Intended Action Description 4.2 Intended Action Description 4.3 Localized Action Execution and 4.4 The Current State with GPT-4V 4.3 Localized Action Execution and 4.4 The Current State with GPT-4V 5 Android Screen Navigation Experiment 5.1 Experimental Setup 5.1 Experimental Setup 5.2 Performance Comparison 5.2 Performance Comparison 5.3 Ablation Studies 5.3 Ablation Studies 5.4 Error Analysis 5.4 Error Analysis 6 Discussion 6 Discussion 7 Conclusion and References 7 Conclusion and References 4.3 Localized Action Execution A natural question is how reliable GPT-4V can convert its understanding of the screen into executable actions. Table 1 shows an accuracy of 74.5% on selecting the location that could lead to the desired outcome. Figure 2 shows the added marks with interactive SAM (Yang et al., 2023b; Kirillov et al., 2023), and the corresponding GPT-4V outputs. As shown in Figure 2(a), GPT-4V can select the “X” symbol (ID: 9) to close the tabs, echoing its previous description in Figure 1(a). GPT-4V is also capable of selecting the correct location to click from the large portion of clickable icons, such as the screen shown in (b). Figure 1(c) represents a complicated screen with various images and icons, where GPT-4V can select the correct mark 8 for the reading the “6 Alerts.” Within a screen with various texts, such as (d), GPT-4V can identify the clickable web links, and locate the queried one with the correct position 18. 4.4 The Current State with GPT-4V From the analytical experiments on iOS screens, we find GPT-4V is capable of performing GUI navigation. Although several types of failure cases still occur, as outlined below, MM-Navigator shows promise for executing multi-screen navigation to fulfill real-world smartphone use cases. We conclude the section with qualitative results on such episode-level navigation queries. Failure cases. Despite the promising results, GPT-4V does make errors in the zero-shot screen navigation task, as shown in Table 1. These errors are illustrated through representative failure cases as follows. (a) GPT-4V might not generate the correct answer in a single step when the query involves knowledge the model lacks. For example, GPT-4V is not aware that only “GPT-4” can support image uploads, hence it fails to click the “GPT-4” icon before attempting to find the image uploading function. (b) Although usually reliable, GPT-4V might still select the incorrect location. An example of this is selecting the mark 15 for the “ChatGPT” app instead of the correct mark 5. (c) In complex scenarios, GPT-4V’s initial guess might not be correct, such as clicking the “numeric ID 21 for the 12-Hour Forecast” instead of the correct answer of mark 19. (d) When the correct clickable area is not marked, like a “+” icon without any marks, GPT-4V cannot identify the correct location and may reference an incorrect mark instead. Finally, we note that many of those single-step failures may be corrected with iterative explorations, leading to the correct episode-level outcome. Failure cases. From single screens to complete episodes. MMNavigator shows an impressive capability in performing GUI navigation in a zero-shot manner. We further extend MM-Navigator from processing a single cellphone screen to recursively processing an episode of screen inputs. Figure 4 shows the qualitative result. In each step, we include the objective, “You are asked to shop for a milk frother, your budget is between $50 and $100.” and its previous action in the prompt to GPT-4V. We show that the model can effectively perform multi-step reasoning to accomplish the given shopping instruction. From single screens to complete episodes. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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