Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?
研究通过几何特性预测异常检测性能，提出伪异常探针以改善无异常数据时的模型选择。
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BIMScript: Material-Aware Structured Scene Programs for BIM Ingestion
研究通过扩展SceneScript语言，加入材料属性，并优化解码速度和定位精度，解决了现有建筑自动导入BIM工具的问题。
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A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images
This study addresses annotation uncertainty and noise in turbid underwater image segmentation through a large-scale multi-annotator investigation involving over one hundred participants. By systematically analyzing noise sources across varying turbidity levels via controlled experiments, privileged information assistance, and ensemble strategies, this work reveals systematic error patterns induced by turbidity in real-world underwater scenarios for the first time. Furthermore, it proposes effective methods to enhance annotation quality and releases an open-source dataset. This research bridges a critical gap in understanding annotation uncertainty within underwater segmentation, providing both theoretical foundations and practical guidelines for constructing high-quality underwater vision benchmarks.
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Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?
研究通过几何特性预测异常检测性能，提出伪异常探针以改善无异常数据时的模型选择。
0 citationsRead paper
BIMScript: Material-Aware Structured Scene Programs for BIM Ingestion
研究通过扩展SceneScript语言，加入材料属性，并优化解码速度和定位精度，解决了现有建筑自动导入BIM工具的问题。
0 citationsRead paper
A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images
This study addresses annotation uncertainty and noise in turbid underwater image segmentation through a large-scale multi-annotator investigation involving over one hundred participants. By systematically analyzing noise sources across varying turbidity levels via controlled experiments, privileged information assistance, and ensemble strategies, this work reveals systematic error patterns induced by turbidity in real-world underwater scenarios for the first time. Furthermore, it proposes effective methods to enhance annotation quality and releases an open-source dataset. This research bridges a critical gap in understanding annotation uncertainty within underwater segmentation, providing both theoretical foundations and practical guidelines for constructing high-quality underwater vision benchmarks.
0 citationsRead paper