引入了一个名为 OVEarth-Bench 的新基准，用于评估开放词汇地球观测能力。该基准通过扩展类别广度和查询多样性，并在零样本协议下支持掩码和边界框定位，解决了现有评估的局限性。初步评估表明，当前方法的性能有限，多模态大语言模型 (MLLM) 表现最强，而地球观测特定方法往往不如通用模型。 AI
影响 该基准旨在指导开发更现实、更多样化的开放词汇地球观测评估方法，有望提高 MLLM 在该领域的性能。
排序理由 该集群描述了一个用于评估特定领域人工智能模型的新学术基准。
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Open-vocabulary Earth observation (EO) aims to localize geospatial concepts specified in natural language rather than a fixed label set. Existing benchmarks, however, usually cover narrow category vocabularies or limited query forms. To fill this gap, we introduce OVEarth-Bench, …
Open-vocabulary Earth observation (EO) aims to localize geospatial concepts specified in natural language rather than a fixed label set. Existing benchmarks, however, usually cover narrow category vocabularies or limited query forms. To fill this gap, we introduce OVEarth-Bench, …
arXiv:2607.27278v1 Announce Type: new Abstract: Open-vocabulary Earth observation (EO) aims to localize geospatial concepts specified in natural language rather than a fixed label set. Existing benchmarks, however, usually cover narrow category vocabularies or limited query forms…