Symmetric Image-Text Tuning With Entropy-Guided Fusion for Online Continual Learning in Non-Stationary Visual Streams

Online continual learning studies how models learn from continuous and non-stationary data streams. In this paper, we observe that CLIP models exhibit an asymmetric image-text interaction under online continual learning. Specifically, text features of previously seen classes may introduce unfavorable supervision when paired with visual features of newly observed data, leading to catastrophic forgetting. To alleviate this issue, we propose a simple yet effective symmetric image-text tuning (SIT) strategy that removes such asymmetric text supervision during online learning. We further introduce an entropy-guided fusion (EGF) mechanism that adaptively combines predictions from the pretrained and finetuned branches based on their relative uncertainty. This design allows the model to recover pretrained knowledge when the finetuned branch becomes unreliable, while still preserving plasticity on recently observed classes when confidence is high. In addition, we present MiD-Blurry, an online continual learning benchmark that combines multiple class distribution patterns to better reflect realistic data streams with blurred temporal boundaries. Extensive experiments on standard continual learning benchmarks and the MiD-Blurry setting evaluate inference-at-any-time performance and generalization to future data. The results show that the proposed approach maintains a practical balance between adapting to new data and preserving previously learned information in realistic online learning scenarios.