May 16, 2024, 4:45 a.m. | Guozhang Liu, Ting Liu, Mengke Yuan, Tao Pang, Guangxing Yang, Hao Fu, Tao Wang, Tongkui Liao

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arXiv:2405.09024v1 Announce Type: new
Abstract: The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism, inspired by the two phase ``early-learning'' and ``memorization'' learning dynamics of …

abstract annotations arxiv dataset detection dynamic effects fine-grained however images labels loss modern object objects robust scale sensing type

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