  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.
