Ranking Distance Calibration for Cross-Domain Few-Shot Learning
@article{Li2021RankingDC, title={Ranking Distance Calibration for Cross-Domain Few-Shot Learning}, author={Pan Li and Shaogang Gong and Yanwei Fu and Chengjie Wang}, journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2021}, pages={9089-9098} }
Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are in different domains. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. This encourages us to explore more information in the target domain rather than to overly elaborate training strategies on the source domain as in many existing methods. Hence, we start from a generic representation pre…
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