Fine-tuning Vision-Language Models for Creative Software Understanding

Tarek Bukhari, Yuna Kim, Wei Zhang, Priya Patel

CTRLOP, Inc. — June 2024

Abstract

We present a methodology for fine-tuning vision-language models specifically for creative software interfaces. Our approach adapts pre-trained VLMs to understand the unique visual and functional characteristics of design tools, video editors, and 3D modeling software. We demonstrate improved performance on creative workflow tasks compared to general-purpose models and discuss the challenges of domain-specific adaptation.

1. Introduction

Vision-language models have shown remarkable capabilities in understanding general visual content, but their performance on specialized domains like creative software remains limited. Creative software interfaces present unique challenges: complex tool layouts, domain-specific terminology, and workflow-specific visual patterns. We propose a fine-tuning approach that adapts pre-trained VLMs to better understand these specialized environments.

2. Related Work

Recent work on vision-language models has focused on general-purpose understanding, with models like CLIP and GPT-4V achieving impressive results on broad visual tasks. However, domain-specific fine-tuning for creative software has received limited attention. Prior work on UI understanding has primarily focused on web and mobile interfaces, leaving creative software largely unexplored.

3. Method

Our fine-tuning approach involves three key components: domain-specific data collection, curriculum learning, and specialized loss functions. We collect screenshots from various creative software applications and annotate them with tool descriptions, workflow steps, and interface element relationships. The curriculum learning strategy gradually increases task complexity, while our loss functions emphasize domain-specific understanding.

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Figure 1: Fine-tuning architecture for creative software VLMs. The diagram shows the pre-trained model, domain-specific data pipeline, and specialized training components. (Image description: A flow diagram showing the fine-tuning process with data collection, model adaptation, and evaluation stages.)

4. Creative Software Dataset

We construct a dataset of 50,000 annotated screenshots from creative software applications including Adobe Creative Suite, Figma, Blender, and DaVinci Resolve. Each annotation includes tool identification, parameter descriptions, and workflow context. The dataset covers diverse creative tasks and interface variations.

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Figure 2: Examples from the creative software dataset. (Image description: Multiple screenshots from different creative software with annotations highlighting tools and interface elements.)

5. Results

Our fine-tuned model achieves significant improvements over baseline VLMs on creative software understanding tasks. We evaluate performance on tool identification, workflow comprehension, and interface navigation. The model shows particular strength in understanding domain-specific terminology and complex interface relationships.

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Figure 3: Performance comparison between fine-tuned and baseline models. (Image description: A bar chart comparing accuracy scores for different creative software understanding tasks.)

6. Discussion

The results demonstrate the value of domain-specific fine-tuning for creative software understanding. Key challenges include maintaining generalization across different creative tools while capturing domain-specific nuances. Future work will explore transfer learning between creative domains and integration with interactive AI assistants.

7. Conclusion

We present an effective approach for fine-tuning vision-language models for creative software understanding. Our methodology improves performance on creative workflow tasks and provides a foundation for AI assistants that can effectively work with creative software interfaces.

References

Radford, A. et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. ICML.
OpenAI. (2023). GPT-4V(ision) System Card. OpenAI.
Devlin, J. et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.

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