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Pusan National University

Academic institutionasia · kr
Official website
Research library79linked papers
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Selected work

Representative Papers

Towards Sparsely Annotated Open-World Object Detection

Aug 12, 2026

This work addresses the challenge of object detection in real-world scenarios where sparse annotations and unknown object categories coexist, leading to ambiguous supervision that hinders differentiation between unlabeled known objects and truly novel ones. To tackle this issue, the paper introduces Sparse Annotation Open-World Object Detection (SA-OWOD), a new task formulation, and proposes DPOD, a unified framework comprising a Known Target Recovery Module (KTRM) that reconstructs complete supervision signals and regularizes the feature space, and a Dual-view Discrepant Target Generator (DDTG) that leverages cross-view semantic discrepancies to identify reliable unknown objects. Evaluated on a sparse-annotation open-world benchmark, DPOD significantly outperforms existing methods, achieving notable gains especially in unknown object detection performance.

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Local-Global Feature Mixer and Trend-Guided Consistent Learning for Remaining Useful Life Prediction of Rotating Machinery

Aug 06, 2026

This work addresses the challenges of error accumulation and violation of degradation irreversibility in existing recursive health indicator prediction methods for long-term remaining useful life (RUL) estimation. To mitigate these issues, the authors propose a hybrid representation mechanism that integrates local and global features to reduce sensitivity to high-frequency noise. Additionally, they introduce a trend-guided recursive consistency loss function, which leverages soft dynamic time warping alignment and a multi-step rollout strategy to effectively bridge the gap between training and inference dynamics. Experimental results on two public bearing datasets demonstrate that the proposed approach significantly enhances the stability of long-term health indicator extrapolation and improves RUL prediction accuracy, while maintaining low computational complexity and strong generalization capability.

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Recent publications

Latest Papers

Towards Sparsely Annotated Open-World Object Detection

Aug 12, 2026

This work addresses the challenge of object detection in real-world scenarios where sparse annotations and unknown object categories coexist, leading to ambiguous supervision that hinders differentiation between unlabeled known objects and truly novel ones. To tackle this issue, the paper introduces Sparse Annotation Open-World Object Detection (SA-OWOD), a new task formulation, and proposes DPOD, a unified framework comprising a Known Target Recovery Module (KTRM) that reconstructs complete supervision signals and regularizes the feature space, and a Dual-view Discrepant Target Generator (DDTG) that leverages cross-view semantic discrepancies to identify reliable unknown objects. Evaluated on a sparse-annotation open-world benchmark, DPOD significantly outperforms existing methods, achieving notable gains especially in unknown object detection performance.

0 citationsRead paper

Local-Global Feature Mixer and Trend-Guided Consistent Learning for Remaining Useful Life Prediction of Rotating Machinery

Aug 06, 2026

This work addresses the challenges of error accumulation and violation of degradation irreversibility in existing recursive health indicator prediction methods for long-term remaining useful life (RUL) estimation. To mitigate these issues, the authors propose a hybrid representation mechanism that integrates local and global features to reduce sensitivity to high-frequency noise. Additionally, they introduce a trend-guided recursive consistency loss function, which leverages soft dynamic time warping alignment and a multi-step rollout strategy to effectively bridge the gap between training and inference dynamics. Experimental results on two public bearing datasets demonstrate that the proposed approach significantly enhances the stability of long-term health indicator extrapolation and improves RUL prediction accuracy, while maintaining low computational complexity and strong generalization capability.

0 citationsRead paper