English(EN) Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning

Trust-SSL 增强了航空图像自监督学习对退化的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-23 07:07

研究人员开发了 Trust-SSL，这是一种新颖的自监督学习策略，旨在提高航空图像分析的鲁棒性。该方法将每样本信任权重引入对齐目标，作为对比损失的加性残差。实验表明，该方法在 EuroSAT、AID 和 NWPU-RESISC45 等基准数据集上显著提高了性能，尤其是在雾霾和运动模糊等严重退化条件下。 AI

影响引入了一种用于鲁棒航空图像分析的新方法，有可能在具有挑战性的环境条件下提高性能。

排序理由这是一篇介绍计算机视觉自监督学习新方法的学术论文。

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Maha Driss · 2026-04-23 07:07

Trust-SSL：用于鲁棒航空自监督学习的加性残差选择性不变性

Self-supervised learning (SSL) is a standard approach for representation learning in aerial imagery. Existing methods enforce invariance between augmented views, which works well when augmentations preserve semantic content. However, aerial images are frequently degraded by haze,…

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Trust-SSL：用于鲁棒航空自监督学习的加性残差选择性不变性

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