OWL-survey
@AikenHong2021 OWL
分析现有的OWL特点,和当前自己的研究做一个区分,也汲取一下别人的研究的要点,
Reference
- arxiv @ self-supervised feature improve open-world learning
- arxiv @ open-world semi-supervised learning
- arxiv @ open-world learning without labels
- arxiv @ unseen class discovery in open-world classification
- arxiv @ Open-World Active Learning with Stacking Ensemble for Self-Driving Cars
- www @ open-world learning and application to product classification
- cvpr @ open world composition zero-shot learning
- cvpr @ Towards Open World Object Detection
- cvpr](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Large-Scale_Long-Tailed_Recognition_in_an_Open_World_CVPR_2019_paper.pdf)) @ Large-Scale Long-Tailed Recognition in an Open World
Conclusion
Papers
Mulit Open world Learning Definition
拒绝未见过的类的实例,逐步学习新的类扩展现有模型
:zap: Large-Scale Long-Tailed Recognition in an Open World
Large-Scale Long-Tailed Recognition in an Open World (liuziwei7.github.io)