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- <!-- benchmark 发布公告 -->
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  <div style="background-color: #ecfdf5; border-left: 4px solid #10b981; padding: 0.75em 1em; margin-top: 1em; color: #065f46; font-weight: bold; border-radius: 0.375em;">
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- 🎉 本仓库为 <strong>RefSpatial</strong> 的新版本 <strong>RefSpatial-Bench-Expand</strong>!<br>
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- 新版本在原始基础上<strong>扩充了室内场景</strong>(如工厂、商店),并新增了<strong>未涵盖的室外场景</strong>(如街道、停车场),更加全面地评测空间指代任务。
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  </div>
 
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  <div style="background-color: #fef3c7; border-left: 4px solid #f59e0b; padding: 0.75em 1em; margin-top: 1em; color: #78350f; font-weight: bold; border-radius: 0.375em;">
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- 🏆 本Benchmark所属的文章<strong>RoboRefer</strong> 已被 <strong>NeurIPS 2025</strong> 正式接收!<br>
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- 感谢大家的关注与支持! 🙌
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  </div>
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+ <!-- New benchmark release announcement -->
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  <div style="background-color: #ecfdf5; border-left: 4px solid #10b981; padding: 0.75em 1em; margin-top: 1em; color: #065f46; font-weight: bold; border-radius: 0.375em;">
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+ 🎉 This repository contains the new version of <strong>RefSpatial-Bench</strong> <strong>RefSpatial-Expand-Bench</strong>!<br>
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+ Based on the original benchmark, the new version <strong>extends indoor scenes</strong> (e.g., factories, stores) and adds <strong>previously uncovered outdoor scenarios</strong> (e.g., streets, parking lots), providing a more comprehensive evaluation of spatial referring tasks.
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  </div>
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  <div style="background-color: #fef3c7; border-left: 4px solid #f59e0b; padding: 0.75em 1em; margin-top: 1em; color: #78350f; font-weight: bold; border-radius: 0.375em;">
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+ 🏆 The paper associated with this benchmark, <strong>RoboRefer</strong>, has been accepted to <strong>NeurIPS 2025</strong>!<br>
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+ Thank you all for your attention and support! 🙌
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  </div>
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