GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting
为解决动态场景重建中内存使用和渲染效率问题，提出几何感知冗余优化（GARO）方法，通过评估和剔除冗余高斯点以提高渲染速度。
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Progressive Pseudo-Label Optimization for Point-Supervised Change Detection
该研究针对点监督变化检测中伪标签不完整和噪声问题，提出两阶段框架结合SAM2先验逐步优化伪标签，提高边界质量和结构一致性。
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Quantization Effects on Bangla Language Understanding in Large Language Models: A Systematic Evaluation
研究通过对比三种模型家族在不同量化格式下的表现，系统评估了量化对孟加拉语理解的影响，发现架构和量化方法的选择比位宽更重要。
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Gaussian process learning with flow map refinement for parameter estimation in dynamical systems
本文针对动力系统参数估计问题，提出了一种结合高斯过程学习与流映射精炼的两阶段方法，以提高在稀疏和噪声观测下的参数估计精度。
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FIRM: Fine-Grained Intra-Token Representation of Masks for Remote Sensing Reasoning Segmentation
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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GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting
为解决动态场景重建中内存使用和渲染效率问题，提出几何感知冗余优化（GARO）方法，通过评估和剔除冗余高斯点以提高渲染速度。
0 citationsRead paper
Progressive Pseudo-Label Optimization for Point-Supervised Change Detection
该研究针对点监督变化检测中伪标签不完整和噪声问题，提出两阶段框架结合SAM2先验逐步优化伪标签，提高边界质量和结构一致性。
0 citationsRead paper
Quantization Effects on Bangla Language Understanding in Large Language Models: A Systematic Evaluation
研究通过对比三种模型家族在不同量化格式下的表现，系统评估了量化对孟加拉语理解的影响，发现架构和量化方法的选择比位宽更重要。
0 citationsRead paper
Gaussian process learning with flow map refinement for parameter estimation in dynamical systems
本文针对动力系统参数估计问题，提出了一种结合高斯过程学习与流映射精炼的两阶段方法，以提高在稀疏和噪声观测下的参数估计精度。
0 citationsRead paper
FIRM: Fine-Grained Intra-Token Representation of Masks for Remote Sensing Reasoning Segmentation
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.
0 citationsRead paper