  Continual Learning (CL) aims to enable Deep Neural Networks (DNNs) to learn
new data without forgetting previously learned knowledge. The key to achieving
this goal is to avoid confusion at the feature level, i.e., avoiding confusion
within old tasks and between new and old tasks. Previous prototype-based CL
methods generate pseudo features for old knowledge replay by adding Gaussian
noise to the centroids of old classes. However, the distribution in the feature
space exhibits anisotropy during the incremental process, which prevents the
pseudo features from faithfully reproducing the distribution of old knowledge
in the feature space, leading to confusion in classification boundaries within
old tasks. To address this issue, we propose the Distribution-Level Memory
Recall (DMR) method, which uses a Gaussian mixture model to precisely fit the
feature distribution of old knowledge at the distribution level and generate
pseudo features in the next stage. Furthermore, resistance to confusion at the
distribution level is also crucial for multimodal learning, as the problem of
multimodal imbalance results in significant differences in feature responses
between different modalities, exacerbating confusion within old tasks in
prototype-based CL methods. Therefore, we mitigate the multi-modal imbalance
problem by using the Inter-modal Guidance and Intra-modal Mining (IGIM) method
to guide weaker modalities with prior information from dominant modalities and
further explore useful information within modalities. For the second key, We
propose the Confusion Index to quantitatively describe a model's ability to
distinguish between new and old tasks, and we use the Incremental Mixup Feature
Enhancement (IMFE) method to enhance pseudo features with new sample features,
alleviating classification confusion between new and old knowledge.
