## 5 Conclusions and Future Work  
In this paper, we introduce IT-Prompt, a novel prompt-based continual learning framework designed for pretrained CLIP models. This framework capitalizes on the synergy between image and text modalities to enhance the models' knowledge base while preserving their zero-shot capabilities and mitigating catastrophic forgetting. Furthermore, we have developed the C-NCM classifier, which obviates the need for storing and retraining samples from old tasks, thereby significantly enhancing training efficiency and reducing resource consumption. Experimental results indicate that our method consistently surpasses existing state-of-the-art approaches across multiple benchmark datasets, demonstrating superior adaptability and robustness in managing dynamic and evolving tasks. These findings underscore the potential of IT-Prompt and the C-NCM classifier to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Prompt Length**} & \multicolumn{5}{c}{**CUB-200**} \\ \cline{2-7}  & \multicolumn{2}{c}{**T5**} & \multicolumn{2}{c}{**T10**} & \multicolumn{2}{c}{**T20**} \\ \cline{2-7}  & **FAA** & **CAA** & **FAA** & **CAA** & **FAA** & **CAA** \\ \hline L-10 & 80.02 & 82.18 & 77.82 & 79.41 & 75.92 & 78.11 \\ L-20 & 80.07 & 82.77 & 78.20 & 80.36 & 77.66 & 79.15 \\ L-40 & **81.11** & **83.85** & **78.87** & **80.65** & **77.77** & **79.93** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overall performance of continual learning with different prompt length. Bold values indicate the best-performing metrics in each comparison.  
Figure 6: Effectiveness of the text guide component. The FAA performance of different experiments (left y-axis). The CAA performance of different experiments (right y-axis). The gain from the text guide is highlighted in orange. Performance on CIFAR100 with 10, 20, 50 steps.  
revolutionize continual learning methodologies, offering an efficient and scalable solution for forthcoming challenges.  
However, IT-Prompt may still face limitations when dealing with highly imbalanced class distributions or domain shifts, as it relies on pre-trained CLIP representations. In incremental learning scenarios with more diverse and heterogeneous data, the fixed nature of CLIP's pre-trained feature space may struggle to accommodate unseen distributions, leading to suboptimal generalization and increased task interference. Future work could explore adaptive prompt tuning strategies that dynamically adjust to varying data distributions and more robust feature alignment techniques to ensure stability across diverse continual learning settings. Additionally, integrating meta-learning or self-supervised adaptation mechanisms could further enhance IT-Prompt's ability to handle evolving and diverse data streams more effectively.  
Conceptualization, [PERSON] and W.F.; methodology, L.J.; validation L.J.; formal analysis, L.J. and W.F.; investigation, [PERSON] and X.C.; data curation, L.J. and X.C.; writing--original draft preparation, L.J.; writing-review and editing, [PERSON] and W.F.; visualization, X.C.; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Social Science Fund of China under grant 24 BG143.  
Not applicable.  
The authors declare no conflicts of interest.