1. Model-agnostic meta-learning for fast adaptation of deep networks;chelsea;Int Conference on Machine Learning,2017
2. Data augmentation generative adversarial net-works;antoniou;ArXiv Preprint,2017
3. Pro-totypical networks for few-shot learning;jake;Advances in neural information processing systems,2017
4. Focal loss for dense object detection;tsung-yi;Proceedings of the IEEE International Conference on Computer Vision,2017
5. Dense relation distillation with context-aware aggregation for few-shot object detection;hanzhe;Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2021