  Continual learning aims to enable a model to incrementally learn knowledge
from sequentially arrived data. Previous works adopt the conventional
classification architecture, which consists of a feature extractor and a
classifier. The feature extractor is shared across sequentially arrived tasks
or classes, but one specific group of weights of the classifier corresponding
to one new class should be incrementally expanded. Consequently, the parameters
of a continual learner gradually increase. Moreover, as the classifier contains
all historical arrived classes, a certain size of the memory is usually
required to store rehearsal data to mitigate classifier bias and catastrophic
forgetting. In this paper, we propose a non-incremental learner, named
AttriCLIP, to incrementally extract knowledge of new classes or tasks.
Specifically, AttriCLIP is built upon the pre-trained visual-language model
CLIP. Its image encoder and text encoder are fixed to extract features from
both images and text. Text consists of a category name and a fixed number of
learnable parameters which are selected from our designed attribute word bank
and serve as attributes. As we compute the visual and textual similarity for
classification, AttriCLIP is a non-incremental learner. The attribute prompts,
which encode the common knowledge useful for classification, can effectively
mitigate the catastrophic forgetting and avoid constructing a replay memory. We
evaluate our AttriCLIP and compare it with CLIP-based and previous
state-of-the-art continual learning methods in realistic settings with
domain-shift and long-sequence learning. The results show that our method
performs favorably against previous state-of-the-arts. The implementation code
can be available at https://github.com/bhrqw/AttriCLIP.
