To explore more about this chapter, refer to the following works:
- Hands-On Meta-Learning: https://www.packtpub.com/big-data-and-business-intelligence/hands-meta-learning-python
- Revisiting local descriptor based image-to-class measure for few-shot learning: https://arxiv.org/pdf/1903.12290.pdf
- Finding task-relevant features for few-shot learning by category traversal: https://arxiv.org/pdf/1905.11116.pdf
- RepMet: Representative-based metric learning for classification and few-shot object detection: https://arxiv.org/abs/1806.04728
- One-shot object detection with co-attention and co-excitation: https://arxiv.org/pdf/1911.12529.pdf
- CANet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning: https://arxiv.org/pdf/1903.02351.pdf
- PANet: Few-shot image semantic segmentation with prototype alignment: https://arxiv.org/pdf/1908...
