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

cs.CV updates on arXiv.org arxiv.org

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 cs.cv dataset detection dynamic effects fine-grained however images labels loss modern object objects robust scale sensing type

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Manager, Business Intelligence

@ Revlon | New York City, United States