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Jiangxi Normal University

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Research library11linked papers
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Selected work

Representative Papers

Contrastive Mask Fidelity: Reference-Free Auditing of Ground-Truth Masks in Remote Sensing Semantic Segmentation

Aug 10, 2026

This study addresses the prevalent issues of coarse, misaligned, or incomplete manual annotations in remote sensing semantic segmentation, which often distort model evaluation. To tackle this, the authors propose a training-free, reference-free mask fidelity assessment method that constructs counterfactual image pairs—preserving and erasing the region within the mask—and leverages a frozen vision-language model to evaluate whether class-specific evidence is concentrated inside the mask and absent outside it. This approach enables, for the first time, reference-free auditing of annotation quality in remote sensing segmentation, revealing systematic labeling biases across categories and facilitating automatic refinement of supervision signals. The proposed Contrastive Mask Fidelity (CMF) metric achieves 81% agreement with expert judgments across ten remote sensing datasets, substantially outperforming existing methods, and CMF-guided supervision significantly enhances cross-domain transfer performance.

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SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation

Jul 23, 2026

This work addresses the challenge of effectively segmenting slender, anisotropic defects—such as cracks and scratches—on steel surfaces, which existing methods struggle to handle accurately. To this end, the authors propose the SPDCN network, which incorporates a Fuzzy-enhanced Multi-scale Context Module (FMCM) to adaptively fuse multi-scale contextual information. Additionally, an Adaptive Direction-Aware Deformable Convolution (ADADC) is introduced, leveraging decoupled horizontal and vertical strip convolutions within a grouped multi-branch architecture enhanced by an intuitionistic fuzzy channel attention mechanism. This design enables precise modeling of defect morphology and dominant orientation. Evaluated on benchmark datasets including NEU-Seg, the proposed method achieves a state-of-the-art mIoU of 89.60% with only 3.54 million parameters, outperforming current advanced approaches.

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Recent publications

Latest Papers

Contrastive Mask Fidelity: Reference-Free Auditing of Ground-Truth Masks in Remote Sensing Semantic Segmentation

Aug 10, 2026

This study addresses the prevalent issues of coarse, misaligned, or incomplete manual annotations in remote sensing semantic segmentation, which often distort model evaluation. To tackle this, the authors propose a training-free, reference-free mask fidelity assessment method that constructs counterfactual image pairs—preserving and erasing the region within the mask—and leverages a frozen vision-language model to evaluate whether class-specific evidence is concentrated inside the mask and absent outside it. This approach enables, for the first time, reference-free auditing of annotation quality in remote sensing segmentation, revealing systematic labeling biases across categories and facilitating automatic refinement of supervision signals. The proposed Contrastive Mask Fidelity (CMF) metric achieves 81% agreement with expert judgments across ten remote sensing datasets, substantially outperforming existing methods, and CMF-guided supervision significantly enhances cross-domain transfer performance.

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SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation

Jul 23, 2026

This work addresses the challenge of effectively segmenting slender, anisotropic defects—such as cracks and scratches—on steel surfaces, which existing methods struggle to handle accurately. To this end, the authors propose the SPDCN network, which incorporates a Fuzzy-enhanced Multi-scale Context Module (FMCM) to adaptively fuse multi-scale contextual information. Additionally, an Adaptive Direction-Aware Deformable Convolution (ADADC) is introduced, leveraging decoupled horizontal and vertical strip convolutions within a grouped multi-branch architecture enhanced by an intuitionistic fuzzy channel attention mechanism. This design enables precise modeling of defect morphology and dominant orientation. Evaluated on benchmark datasets including NEU-Seg, the proposed method achieves a state-of-the-art mIoU of 89.60% with only 3.54 million parameters, outperforming current advanced approaches.

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