Authors: Ruei-Che Chang、Yuxuan Liu、Lotus Zhang、Anhong Guo Paper: https://arxiv.org/abs/2408.06632 Introduction Images are a crucial part of our daily lives, serving various purposes such as work, social engagement, and entertainment. However, blind and low-vision (BLV) individuals often face significant barriers when it comes to image editing. Traditional image editing tools require visual evaluation and manipulation, making them inaccessible to BLV users. To address this issue, the researchers developed EditScribe, a prototype system that enables non-visual image editing through natural language verification loops powered by large multimodal models (LMMs). Abstract EditScribe allows BLV users to comprehend image content through initial general and object…
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