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China Agricultural University

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Selected work

Representative Papers

FIRM: Fine-Grained Intra-Token Representation of Masks for Remote Sensing Reasoning Segmentation

Aug 14, 2026

This study addresses the issues of coarse boundaries and instance adhesion in remote sensing semantic segmentation caused by multi-target mixing within visual tokens. To overcome these limitations, we propose FIRM, a novel method that innovatively introduces intra-token sub-unit mask representations and a lightweight continuous rendering mechanism. By transcending single-label constraints through sub-unit prediction, lookup table transformation, and soft structural field marginalization, FIRM achieves fine-grained segmentation. Extensive experiments demonstrate state-of-the-art performance across five benchmarks. Notably, on the LASER dataset, FIRM attains GIoU/CIoU scores of 70.5/80.5 and improves the EarthReason metric by 3.0 points, significantly enhancing segmentation accuracy in complex scenes.

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IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection

Aug 14, 2026

This study addresses the challenges of autonomous driving object detection arising from the sparsity and disorderliness of radar point clouds by proposing a translation-rotation invariant graph representation coupled with a virtual node message-passing neural network. Through invariant feature reconstruction, an enhanced message-passing mechanism, and residual connections, the method effectively strengthens feature propagation and global modeling capabilities while balancing model robustness with inference efficiency. Experimental evaluations on the RadarScenes dataset demonstrate that this approach outperforms state-of-the-art methods and significantly reduces computational and memory overhead. Consequently, this work establishes a novel paradigm for efficient radar-based object detection in autonomous systems.

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Latest Papers

FIRM: Fine-Grained Intra-Token Representation of Masks for Remote Sensing Reasoning Segmentation

Aug 14, 2026

This study addresses the issues of coarse boundaries and instance adhesion in remote sensing semantic segmentation caused by multi-target mixing within visual tokens. To overcome these limitations, we propose FIRM, a novel method that innovatively introduces intra-token sub-unit mask representations and a lightweight continuous rendering mechanism. By transcending single-label constraints through sub-unit prediction, lookup table transformation, and soft structural field marginalization, FIRM achieves fine-grained segmentation. Extensive experiments demonstrate state-of-the-art performance across five benchmarks. Notably, on the LASER dataset, FIRM attains GIoU/CIoU scores of 70.5/80.5 and improves the EarthReason metric by 3.0 points, significantly enhancing segmentation accuracy in complex scenes.

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IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection

Aug 14, 2026

This study addresses the challenges of autonomous driving object detection arising from the sparsity and disorderliness of radar point clouds by proposing a translation-rotation invariant graph representation coupled with a virtual node message-passing neural network. Through invariant feature reconstruction, an enhanced message-passing mechanism, and residual connections, the method effectively strengthens feature propagation and global modeling capabilities while balancing model robustness with inference efficiency. Experimental evaluations on the RadarScenes dataset demonstrate that this approach outperforms state-of-the-art methods and significantly reduces computational and memory overhead. Consequently, this work establishes a novel paradigm for efficient radar-based object detection in autonomous systems.

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