Online Lifelong Learning (OLL) addresses the challenge of learning from
continuous and non-stationary data streams. Existing online lifelong learning
methods based on image classification models often require preset conditions
such as the total number of classes or maximum memory capacity, which hinders
the realization of real never-ending learning and renders them impractical for
real-world scenarios. In this work, we propose that vision-language models,
such as Contrastive Language-Image Pretraining (CLIP), are more suitable
candidates for online lifelong learning. We discover that maintaining symmetry
between image and text is crucial during Parameter-Efficient Tuning (PET) for
CLIP model in online lifelong learning. To this end, we introduce the Symmetric
Image-Text (SIT) tuning strategy. We conduct extensive experiments on multiple
lifelong learning benchmark datasets and elucidate the effectiveness of SIT
through gradient analysis. Additionally, we assess the impact of lifelong
learning on generalizability of CLIP and found that tuning the image encoder is
beneficial for lifelong learning, while tuning the text encoder aids in
zero-shot learning.

在线终身学习 (OLL) 研究了从连续和非恒定数据流中学习的挑战。通过对视觉 - 语言模型 (如 Contrastive Language-Image Pretraining，CLIP) 进行参数高效调整 (PET) 的在线终身学习方法显示了对图像和文本之间的对称性的重要性，并且引入了 Symmetric Image-Text (SIT) 调整策略。通过梯度分析的实验证明了 SIT 的有效性，此外还评估了终身学习对 CLIP 的泛化能力的影响，并发现调整图像编码器对终身学习有益，而调整文本编码器有助于零样本学习。