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@@ -14,7 +14,7 @@ Wan Ju Kang, Eunki Kim, Na Min An, Sangryul Kim, Haemin Choi, Ki Hoon Kwak, Jame
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  ## 📄 [Paper (coming soon)](URL)    
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  Hello, we are a team of researchers based in KAIST AI working on accessible visualization.
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- In specific, we compiled a diagram description dataset for the blind and low-vision individuals.
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  We worked in close cooperation with two schools for the blind, as well as over 30 sighted annotators, and we are grateful for their contribution.
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  Check out our preprint [coming soon], and feel free to contact us at [email protected].
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@@ -38,7 +38,7 @@ demonstrate their fine-tuning potential in various downstream tasks.
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  - SightationReasoning
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/cNshK4QAdiNMqk7x6J6j7.png" width="100%" height="100%" title="visual_abstract" alt="visual_abstract"></img>
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- The key benefit of utilizing sighted user feedback lies in their assessments that are based on solid visual
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  grounding. The compiled assessments prove an effective training substance for steering VLMs towards more
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  accessible descriptions.
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/8oYvtq7dtv_Ck-U6OlcAE.png" width="70%" height="70%" title="dimensions_assignment" alt="dimensions_assignment"></img>
 
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  ## 📄 [Paper (coming soon)](URL) &nbsp;&nbsp;&nbsp;
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  Hello, we are a team of researchers based in KAIST AI working on accessible visualization.
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+ In specific, we compiled a diagram description dataset for blind and low-vision (BLV) individuals.
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  We worked in close cooperation with two schools for the blind, as well as over 30 sighted annotators, and we are grateful for their contribution.
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  Check out our preprint [coming soon], and feel free to contact us at [email protected].
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  - SightationReasoning
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/cNshK4QAdiNMqk7x6J6j7.png" width="100%" height="100%" title="visual_abstract" alt="visual_abstract"></img>
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+ The key benefit of utilizing sighted user feedback lies in their assessments, which are based on solid visual
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  grounding. The compiled assessments prove an effective training substance for steering VLMs towards more
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  accessible descriptions.
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/8oYvtq7dtv_Ck-U6OlcAE.png" width="70%" height="70%" title="dimensions_assignment" alt="dimensions_assignment"></img>