  Few-shot class-incremental learning (FSCIL) aims to adapt the model to new
classes from very few data (5 samples) without forgetting the previously
learned classes. Recent works in many-shot CIL (MSCIL) (using all available
training data) exploited pre-trained models to reduce forgetting and achieve
better plasticity. In a similar fashion, we use ViT models pre-trained on
large-scale datasets for few-shot settings, which face the critical issue of
low plasticity. FSCIL methods start with a many-shot first task to learn a very
good feature extractor and then move to the few-shot setting from the second
task onwards. While the focus of most recent studies is on how to learn the
many-shot first task so that the model generalizes to all future few-shot
tasks, we explore in this work how to better model the few-shot data using
pre-trained models, irrespective of how the first task is trained. Inspired by
recent works in MSCIL, we explore how using higher-order feature statistics can
influence the classification of few-shot classes. We identify the main
challenge of obtaining a good covariance matrix from few-shot data and propose
to calibrate the covariance matrix for new classes based on semantic similarity
to the many-shot base classes. Using the calibrated feature statistics in
combination with existing methods significantly improves few-shot continual
classification on several FSCIL benchmarks. Code is available at
https://github.com/dipamgoswami/FSCIL-Calibration.
