  Continual Learning (CL) enables machine learning models to learn from
continuously shifting new training data in absence of data from old tasks.
Recently, pretrained vision transformers combined with prompt tuning have shown
promise for overcoming catastrophic forgetting in CL. These approaches rely on
a pool of learnable prompts which can be inefficient in sharing knowledge
across tasks leading to inferior performance. In addition, the lack of
fine-grained layer specific prompts does not allow these to fully express the
strength of the prompts for CL. We address these limitations by proposing
ConvPrompt, a novel convolutional prompt creation mechanism that maintains
layer-wise shared embeddings, enabling both layer-specific learning and better
concept transfer across tasks. The intelligent use of convolution enables us to
maintain a low parameter overhead without compromising performance. We further
leverage Large Language Models to generate fine-grained text descriptions of
each category which are used to get task similarity and dynamically decide the
number of prompts to be learned. Extensive experiments demonstrate the
superiority of ConvPrompt and improves SOTA by ~3% with significantly less
parameter overhead. We also perform strong ablation over various modules to
disentangle the importance of different components.
