## 4 Experiments and Analysis
### Results  
We compare IT-Prompt with other CLIP-based methods on the CIFAR-100, TinyImageNet, ImageNet-R, and CUB-200 datasets with different task sequences. From Tables 1 and 2, it is evident that our proposed approach offers substantial advantages over existing pretrained CLIP-based continual learning methods. Specifically, when evaluated on the CIFAR-100 dataset across diverse task sequences, both the FAA and CAA metrics demonstrate significant performance improvements, with enhancements exceeding 10% compared to other methods. In addition, on the TinyImageNet dataset, our method outperforms the state-of-the-art ZSCL approach, achieving nearly a 10% improvement in FAA and a 5% improvement in CAA. These results underscore the robustness and efficacy of our approach in comparison to other methods in the domain. Upon evaluation on the challenging ImageNet-R dataset, which incorporates more complex samples from ImageNet, and the intricate CUB-200 dataset, renowned for its fine-grained distinctions, our method stands out with promising performance improvements. Specifically, on the ImageNet-R dataset, across various task sequences, both the FAA and CAA metrics show varying degrees of performance enhancement. Furthermore, on the CUB-200 dataset, our method achieves an remarkable nearly 20% increase in FAA compared to the ZSCL method across different task sequences. The superior performance of IT-Prompt is driven by its task-adaptive prompt tuning, which dynamically adjusts prompts to capture task-specific nuances while maintaining generalization, reducing task interference. Additionally, the C-NCM classifier eliminates the need for storing and retraining old task samples by leveraging class prototypes, maintaining strong classification boundaries, and preventing long-term forgetting. Unlike previous methods that overlook textual information, IT-Prompt preserves CLIP's zero-shot capabilities by ensuring effective multi-modal synergy between visual and textual features, \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**CIFAR-100**} & \multicolumn{6}{c}{**TinyImageNet**} \\ \cline{2-11}
**Method** & \multicolumn{2}{c}{**T10**} & \multicolumn{2}{c}{**T20**} & \multicolumn{2}{c}{**T50**} & \multicolumn{2}{c}{**T5**} & \multicolumn{2}{c}{**T10**} & \multicolumn{2}{c}{**T20**} \\ \cline{2-11}  & **FAA** & **CAA** & **FAA** & **CAA** & **FAA** & **CAA** & **CAA** & **CAA** & **CAA** & **CAA** & **FAA** & **CAA** \\ \hline Continual-CLIP & 66.72 & 75.17 & 66.72 & 75.95 & 66.72 & 76.49 & 66.43 & 70.49 & 66.43 & 70.55 & 66.43 & 70.51 \\ LwF-VR & 70.75 & 78.81 & 63.54 & 74.54 & 59.45 & 71.02 & 70.89 & 77.56 & 67.05 & 74.12 & 63.89 & 69.94 \\ ZSCL & 73.65 & 82.15 & 69.58 & 80.39 & 67.36 & 79.92 & 73.57 & 80.27 & 71.62 & 78.61 & 68.30 & 77.18 \\ AttrickIP & 81.4 & — & — & — & — & — & — & — & — & — & — & — \\ IT-Prompt (Softmax+Replay) & **88.41** & **91.98** & **88.05** & **92.32** & **85.88** & **91.79** & **82.11** & **84.8** & **81.72** & **84.63** & **77.64** & **82.45** \\ IT-Prompt (C-NCM) & **87.01** & **90.88** & **88.15** & **92.82** & **86.08** & **92.09** & **81.01** & **83.9** & **82.22** & **85.03** & **78.04** & **83.34** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall performance of the CLIP-based continual learning on CIFAR100 and TinyImageNet with different task sequences. We present final average accuracy (FAA\(\uparrow\)) and cumulative average accuracy (CAA\(\uparrow\)). Bold values indicate the best-performing metrics in each comparison.