EditScribe: Non-Visual Image Editing with Natural Language Verification Loops

• URL: http://arxiv.org/abs/2408.06632v1
• Date: Tue, 13 Aug 2024 04:40:56 GMT
• Title: EditScribe: Non-Visual Image Editing with Natural Language Verification Loops
• Authors: Ruei-Che Chang, Yuxuan Liu, Lotus Zhang, Anhong Guo,
• Abstract summary: EditScribe is a prototype system that makes image editing accessible using natural language verification loops powered by large multimodal models. The user first comprehends the image content through initial general and object descriptions, then specifies edit actions using open-ended natural language prompts. In a study with ten blind or low-vision users, we found that EditScribe supported participants to perform and verify image edit actions non-visually.
• Score: 12.16675723509151
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Image editing is an iterative process that requires precise visual evaluation and manipulation for the output to match the editing intent. However, current image editing tools do not provide accessible interaction nor sufficient feedback for blind and low vision individuals to achieve this level of control. To address this, we developed EditScribe, a prototype system that makes image editing accessible using natural language verification loops powered by large multimodal models. Using EditScribe, the user first comprehends the image content through initial general and object descriptions, then specifies edit actions using open-ended natural language prompts. EditScribe performs the image edit, and provides four types of verification feedback for the user to verify the performed edit, including a summary of visual changes, AI judgement, and updated general and object descriptions. The user can ask follow-up questions to clarify and probe into the edits or verification feedback, before performing another edit. In a study with ten blind or low-vision users, we found that EditScribe supported participants to perform and verify image edit actions non-visually. We observed different prompting strategies from participants, and their perceptions on the various types of verification feedback. Finally, we discuss the implications of leveraging natural language verification loops to make visual authoring non-visually accessible.

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