English(EN) RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

RelateAnything模型支持实时开放词汇关系预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了RelateAnything，一个新颖的5300万参数模型，能够实时预测图像中对象之间的关系。与之前的场景图模型不同，RelateAnything在推理时接受自由文本谓词词汇，这意味着它不受预定义关系集的限制。这种灵活性得益于其不依赖对象标签进行关系预测的架构，以及在新语料库RA-4M上的训练，该语料库包含近50万张图像中的400多万个关系。该模型在各种基准测试中取得了显著的性能提升，其性能超过现有开放词汇方法的2.3到3.5倍。 AI

影响该模型处理开放词汇关系的能力可以显著推进场景理解和多模态AI能力。

排序理由该集群描述了一篇详细介绍新AI模型和数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 30 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇详细介绍新AI模型和数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CV TIER_1 English(EN) · Ma\"elic Neau · 2026-09-14 04:00

RelateAnything：从任何输入进行实时开放词汇关系预测

arXiv:2609.12552v1 Announce Type: new Abstract: Open-vocabulary detection accepts any class list at inference, and promptable segmentation returns regions without class names: the taxonomy has left the model and become an input. Relation prediction has not. Scene-graph models are…

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RelateAnything：从任何输入进行实时开放词汇关系预测

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