Web: http://arxiv.org/abs/2204.05072

Sept. 20, 2022, 1:13 a.m. | Karim Guirguis, George Eskandar, Matthias Kayser, Bin Yang, Juergen Beyerer

cs.CV updates on arXiv.org arxiv.org

Few-shot object detection (FSOD) has thrived in recent years to learn novel
object classes with limited data by transferring knowledge gained on abundant
base classes. FSOD approaches commonly assume that both the scarcely provided
examples of novel classes and test-time data belong to the same domain.
However, this assumption does not hold in various industrial and robotics
applications, where a model can learn novel classes from a source domain while
inferring on classes from a target domain. In this work, …

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