Oct 22, 2021 · Few-shot image classification aims to recognize unseen categories with only a few labeled training samples. Recent metric-based approaches ...

Few-shot image classification aims to recognize unseen categories with only a few labeled training samples. Recent metric-based approaches tend to represent ...

Few-shot image classification aims to recognize unseen categories with only a few labeled training samples. Recent metric-based approaches tend to represent ...

Chen, H., Li, H., Li, Y., Chen, C.: Multi-level metric learning for few-shot image recognition. · Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ...

Specifically, our approach involves two individual metric-based networks, such as prototypical networks and relational networks, mutually supplying each other ...

We present a simple method to effectively approximate the underlying distribution of a class by using multiple prototype learning.

Few-shot learning is widely applied in the current stage for remote sensing image classification to use prior knowledge to identify new classes faster.

Its re-popularity in light of the deep learning develop- ment is mainly in image classification. This work focuses on few- shot semantic segmentation, which is ...

Jan 5, 2023 · (2022) propose a few-shot hierarchical classification model using multi-granularity relation networks (HMRN) that takes the inner-class ...

To address these problems, we propose a mutual-guidance network (MGNet) for few-shot semantic segmentation to enhance the discriminative ability of class- ...