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README.md
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@@ -37,7 +37,7 @@ demonstrate their fine-tuning potential in various downstream tasks.
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- SightationVQA
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- SightationReasoning
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<img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/cNshK4QAdiNMqk7x6J6j7.png" width="
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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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The description qualities assessed by their respective evaluator groups.
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## Results
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<img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/094e9Hw7lauvT1tshg1Wj.png" width="
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Tuning VLMs on Sightation enhanced various qualities of the diagram descriptions, evaluated by BLV educators, and shown here as normalized ratings averaged in each aspect.
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The capability of the dataset is most strongly pronounced with Qwen2-VL-2B model, shown above.
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- SightationVQA
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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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43 |
accessible descriptions.
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The description qualities assessed by their respective evaluator groups.
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## Results
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+
<img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/094e9Hw7lauvT1tshg1Wj.png" width="90%" height="90%" title="spider_chart" alt="spider_chart"></img>
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Tuning VLMs on Sightation enhanced various qualities of the diagram descriptions, evaluated by BLV educators, and shown here as normalized ratings averaged in each aspect.
|
50 |
The capability of the dataset is most strongly pronounced with Qwen2-VL-2B model, shown above.
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51 |
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