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README.md
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@@ -14,9 +14,7 @@ The CompreCap benchmark is characterized by human-annotated scene graph and focu
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It provides new semantic segmentation annotations for common objects in images, with an average mask coverage of 95.83%.
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Beyond the careful annotation of objects, CompreCap also includes high-quality descriptions of the attributes bound to the objects, as well as directional relation descriptions between the objects, composing a complete and directed scene graph structure:
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<img src="graph_anno.png" alt="CompreCap" width="500" height="auto">
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The annotations of segmentation masks, category names, the descriptions of attributes and relationships are saved in [./anno.json](https://huggingface.co/datasets/FanLu31/CompreCap/blob/main/anno.json). Based on the CompreCap benchmark, researchers can comprehensively accessing the quality of image captions generated by large vision-language models.
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It provides new semantic segmentation annotations for common objects in images, with an average mask coverage of 95.83%.
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Beyond the careful annotation of objects, CompreCap also includes high-quality descriptions of the attributes bound to the objects, as well as directional relation descriptions between the objects, composing a complete and directed scene graph structure:
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<div align="center">
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<img src="graph_anno.png" alt="CompreCap" width="1000" height="auto">
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</div>
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The annotations of segmentation masks, category names, the descriptions of attributes and relationships are saved in [./anno.json](https://huggingface.co/datasets/FanLu31/CompreCap/blob/main/anno.json). Based on the CompreCap benchmark, researchers can comprehensively accessing the quality of image captions generated by large vision-language models.
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