面向遥感开放世界检测的无提示未知标签生成
Prompt-Free Unknown Label Generation for Open World Detection in Remote Sensing
提出无需外部提示的未知类别发现与语义标注框架，利用层次语义图与场景上下文合成文本嵌入，可直接用于开放世界检测器的词汇扩展与自主标注。
原文依据 This work presents HSGDet, a detector that achieves both discovery and semantic assignment at test time without external prompts.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论遥感中的自主目标检测需要系统能够在部署期间发现新类别并为其分配可用标签。现有的开放世界目标检测器能够识别未知物体，但在人工标注之前不会为其命名。相比之下，开放词表检测器仅在测试时提供提示的情况下才能识别未见类别，缺乏自主发现或命名能力。本文提出HSGDet，一种在测试时无需外部提示即可同时实现发现和语义分配的检测器。该方法引入DHGA，通过导航层次语义图对检测到的物体进行场景条件化的由粗到细分类。它利用周围场景上下文的空间共现模式来产生分类置信度分数。高分区域被识别为已知物体，而低分区域被标记为未知检测。未知区域传递给CR2T，后者通过融合视觉特征、层次父节点和场景上下文来合成文本嵌入，从而实现无提示标注和词汇扩展。该方法无需外部模型即可实现无提示语义标注并支持自主词汇扩展。结果表明，HSGDet在已知平均精度均值上大幅领先最优方法6.6个点，在未知召回率上领先9.9个点。它还将Wilderness Impact降低了36%，从而实现了可扩展的自主航空监测。
英文原文摘要 1482 字符
Autonomous object detection in remote sensing requires systems that can discover new categories and assign them usable labels during deployment. Existing Open-World Object Detectors identify unknown objects but leave them unnamed until manual annotation. In contrast, Open-Vocabulary Detectors recognize unseen categories only with provided prompts at test time, lacking autonomous discovery or naming. This work presents HSGDet, a detector that achieves both discovery and semantic assignment at test time without external prompts. This method introduces DHGA that navigates a hierarchical semantic graph to perform scene-conditioned coarse-to-fine classification of detected objects. It leverages spatial co-occurrence patterns from surrounding scene context to produce classification confidence scores. High-scoring regions are identified as known objects, while low-scoring regions are flagged as unknown detections. Unknown regions pass to CR2T, which synthesizes text embeddings by fusing visual features, hierarchical parents, and scene context, enabling prompt-free labeling and vocabulary expansion. This approach enables prompt-free semantic labeling and supports autonomous vocabulary expansion without requiring external models. Results demonstrate that HSGDet outperforms state-of-the-art methods by a large margin of 6.6 points in Known mAP and 9.9 points in Unknown Recall. It also reduces Wilderness Impact by 36%, enabling scalable and autonomous aerial monitoring.