Few-shot continual learning (FSCL) has attracted intensive attention and
achieved some advances in recent years, but now it is difficult to again make a
big stride in accuracy due to the limitation of only few-shot incremental
samples. Inspired by distinctive human cognition ability in life learning, in
this work, we propose a novel Big-model driven Few-shot Continual Learning
(B-FSCL) framework to gradually evolve the model under the traction of the
world's big-models (like human accumulative knowledge). Specifically, we
perform the big-model driven transfer learning to leverage the powerful
encoding capability of these existing big-models, which can adapt the continual
model to a few of newly added samples while avoiding the over-fitting problem.
Considering that the big-model and the continual model may have different
perceived results for the identical images, we introduce an instance-level
adaptive decision mechanism to provide the high-level flexibility cognitive
support adjusted to varying samples. In turn, the adaptive decision can be
further adopted to optimize the parameters of the continual model, performing
the adaptive distillation of big-model's knowledge information. Experimental
results of our proposed B-FSCL on three popular datasets (including CIFAR100,
minilmageNet and CUB200) completely surpass all state-of-the-art FSCL methods.

在这项工作中，我们提出了一种新颖的大模型驱动的少样本迭代学习（B-FSCL）框架，通过借鉴世界上的大模型（如人类累积知识）的强大编码能力，逐步演化模型，以提高持续模型的适应能力，并优化参数，实现自适应蒸馏的大模型的知识信息。经过在三个流行数据集上的实验验证，我们提出的 B-FSCL 完全超过了所有最先进的 FSCL 方法。