Few-Shot Class-Incremental Learning (FSCIL)presents an extension of the Class Incremental Learning (CIL)problem where a model is faced with the problem of data scarcity while addressing the Catastrophic Forgetting (CF)problem. This problem remains an open problem because all recent works are built...
Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In this paper, we show through empirical results that adopting the ...
However, this neat feature of MANNs has not been exploited in CIL or FSCIL. This is mainly due to the fact that the interference between different class prototypes increases with a grow- ing number of classes, as experienced in FSCIL. To allow continual learning in MANNs, the ...
o few-shot learning é definido pela natureza do problema de aprendizado e não pelo uso de qualquer método ou estrutura de modelo específico.- Os métodos de few-shot learning variam muito, desde a adaptação de modelos pré-treinados para uso em tarefas...
Acknowledgements This work was supported by Coun- cil for Science, Technology and Innovation (CSTI), cross- ministerial Strategic Innovation Promotion Program (SIP), "Innovative AI Hospital System" (Funding Agency: Na- tional Institute of Biomedical Innovation, Health ...
Although existing incremental learning techniques have attempted to address this issue, they still struggle with only few labeled data, particularly when the samples are from varied domains. In this paper, we explore the cross-domain few-shot incremental learning (CDFSCIL) problem. CDFSCIL requires ...
ALFSCIL [34] 80.75 55.17 68.32 25.58 81.27 53.31 65.60 27.96 79.79 59.30 74.32 20.49 69.41 24.68 F2M [46] 64.71 44.67 69.03 20.04 67.28 44.65 66.36 22.63 81.07 60.26 74.33 20.81 69.91 21.16 FCIL [16] 77.12 52.02 67.45 25.10 76.34 52.76 69.11 23.58 78.70 58.48 74.31 20.22 70.29 22.97 WaRP ...
因此,CIL方法寻求通过保持旧类的可判别性来提高后向兼容性,而FSCIL方法通过固定嵌入模块并纳入新的类别。 目前的方法集中在向后兼容上。这就把克服遗忘的负担转移到了后来的模型。然而,如果前一个模型工作得不好,后一个模型也会随之退化。不可能在有限的实例中保持向后兼容递增阶段。以软件开发为例。如果早期的...
本文首次将自蒸馏引入FSCIL,利用自蒸馏提高模型的特征判别性和分类精度。通过在训练过程中增强特征信息,模型能够更好地适应新类的特征。 虚拟类的构建 采用双分支虚拟类间蒸馏的方法,结合mixup和CutMix技术,构建虚拟类以增强特征空间的扩展性。这种方法提高了类间距离,改善了新类的分类性能。 在这里插入图片描述 实验...
Following the FS- CIL setting, later works propose strategies such as vector quantization [6] and calibrated classifiers [21] to improve classification performance. ONCE [37] and iMTFA [11] in- troduce the idea of few-shot incremental learning to object detection and ...