Feb. 8, 2024, 5:47 a.m. | Fan Wu Jinling Gao Lanqing Hong Xinbing Wang Chenghu Zhou Nanyang Ye

cs.CV updates on arXiv.org arxiv.org

In this paper, we focus on a realistic yet challenging task, Single Domain Generalization Object Detection (S-DGOD), where only one source domain's data can be used for training object detectors, but have to generalize multiple distinct target domains. In S-DGOD, both high-capacity fitting and generalization abilities are needed due to the task's complexity. Differentiable Neural Architecture Search (NAS) is known for its high capacity for complex data fitting and we propose to leverage Differentiable NAS to solve S-DGOD. However, it …


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