Image UI Grounding for Creative Workflows: Model and Benchmark

Jane Doe, John Smith, and the CTRLOP Research Team

CTRLOP, Inc. — June 2024

Abstract

This report presents a model for image user interface (UI) grounding tailored to creative software, and introduces a new benchmark for evaluating AI understanding of creative workflows. Existing benchmarks do not adequately capture the complexity of creative software interfaces. We describe our model architecture, the construction of our benchmark, and report initial results.

1. Introduction

Creative software, such as graphic design and video editing tools, presents unique challenges for computer vision systems. User interfaces in these domains are complex, with diverse elements and dynamic layouts. Accurate UI grounding — the ability to identify and localize interface components in images — is essential for intelligent assistance and automation. However, current benchmarks are not representative of these creative environments. This work addresses this gap by proposing a new model and benchmark for image UI grounding in creative workflows.

2. Related Work

Prior research on UI grounding has focused on web and mobile interfaces, with benchmarks such as PubLayNet and RICO. These datasets do not reflect the complexity of creative software. Recent advances in vision-language models have improved general UI understanding, but performance on creative tools remains underexplored.

3. Method

Our model is based on a vision transformer backbone, adapted for the unique characteristics of creative software UIs. The architecture incorporates multi-scale feature extraction and a component relationship module to capture spatial and semantic dependencies between UI elements.

[Model Architecture Diagram Placeholder]

Figure 1: Model architecture for image UI grounding. The diagram illustrates the vision transformer backbone, multi-scale feature extraction, and component relationship module. (Image description: A block diagram showing an input image, transformer layers, feature maps at multiple scales, and a module connecting UI components.)

4. Creative UI Benchmark

We introduce a benchmark dataset consisting of annotated screenshots from a variety of creative software applications. The benchmark includes diverse tasks such as object localization, component classification, and relationship identification. Annotation guidelines were developed to ensure consistency and coverage of creative workflows.

[Benchmark Example Image Placeholder]

Figure 2: Example from the creative UI benchmark. (Image description: A screenshot of a graphic design tool with bounding boxes and labels for toolbars, canvas, and panels.)

5. Results

We evaluate our model on the proposed benchmark and compare it to baseline methods. Metrics include mean average precision (mAP) for localization and F1 score for component classification. Our model demonstrates improved performance over baselines, particularly in complex, multi-element scenes.

[Results Graph Placeholder]

Figure 3: Model performance on the creative UI benchmark. (Image description: A bar graph comparing mAP and F1 scores for our model and baseline models.)

6. Discussion

The results indicate that specialized architectures and benchmarks are necessary for progress in creative UI understanding. Limitations include the scope of the benchmark and the need for further validation on additional creative tools. Future work will address these limitations and explore integration with interactive AI assistants.

7. Conclusion

We present a model and benchmark for image UI grounding in creative software. Our approach improves performance on complex creative interfaces and provides a foundation for future research in this area.

References

Li, Y. et al. (2019). PubLayNet: Largest Dataset Ever for Document Layout Analysis. ICDAR.
Deka, B. et al. (2017). RICO: A Mobile App Dataset for Building Data-Driven Design Applications. UIST.
Dosovitskiy, A. et al. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.

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