Recent advancements in deep learning have demonstrated remarkable performance
comparable to human capabilities across various supervised computer vision
tasks. However, the prevalent assumption of having an extensive pool of
training data encompassing all classes prior to model training often diverges
from real-world scenarios, where limited data availability for novel classes is
the norm. The challenge emerges in seamlessly integrating new classes with few
samples into the training data, demanding the model to adeptly accommodate
these additions without compromising its performance on base classes. To
address this exigency, the research community has introduced several solutions
under the realm of few-shot class incremental learning (FSCIL).
In this study, we introduce an innovative FSCIL framework that utilizes
language regularizer and subspace regularizer. During base training, the
language regularizer helps incorporate semantic information extracted from a
Vision-Language model. The subspace regularizer helps in facilitating the
model's acquisition of nuanced connections between image and text semantics
inherent to base classes during incremental training. Our proposed framework
not only empowers the model to embrace novel classes with limited data, but
also ensures the preservation of performance on base classes. To substantiate
the efficacy of our approach, we conduct comprehensive experiments on three
distinct FSCIL benchmarks, where our framework attains state-of-the-art
performance.

我们介绍了一种创新的 FSCIL 框架，利用语言正则化器和子空间正则化器，该框架不仅能够使模型拥抱有限数据的新类别，还能确保对基础类别的性能保持，通过在基础训练阶段利用视觉 - 语言模型提取的语义信息和在增量训练阶段促进模型对图像和文本语义之间微妙连接的学习。