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Multi-Source Domain Adaptation for Object Detection
[article]
2021
arXiv
pre-print
To reduce annotation labor associated with object detection, an increasing number of studies focus on transferring the learned knowledge from a labeled source domain to another unlabeled target domain. However, existing methods assume that the labeled data are sampled from a single source domain, which ignores a more generalized scenario, where labeled data are from multiple source domains. For the more challenging task, we propose a unified Faster R-CNN based framework, termed Divide-and-Merge
arXiv:2106.15793v1
fatcat:7bq2o3g6t5h73hd4oriwymgfem