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Transductive Few-Shot Classification on the Oblique Manifold
[article]
2021
arXiv
pre-print
Few-shot learning (FSL) attempts to learn with limited data. In this work, we perform the feature extraction in the Euclidean space and the geodesic distance metric on the Oblique Manifold (OM). Specially, for better feature extraction, we propose a non-parametric Region Self-attention with Spatial Pyramid Pooling (RSSPP), which realizes a trade-off between the generalization and the discriminative ability of the single image feature. Then, we embed the feature to OM as a point. Furthermore, we
arXiv:2108.04009v1
fatcat:q2qdzvob4rennckiazr7cfkm5m