## 4 Experiments and Analysis
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**ImageNet-R**} & \multicolumn{6}{c}{**CUB-200**} \\ \cline{2-11}
**Method** & \multicolumn{2}{c}{**T5**} & \multicolumn{2}{c}{**T10**} & \multicolumn{2}{c}{**T20**} & \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 & 73.97 & 78.91 & 73.97 & 79.81 & 73.97 & 80.36 & 51.12 & 60.35 & 51.12 & 62.13 & 51.12 & 63.79 \\ CaRL & 80.67 & 86.46 & 78.03 & 84.66 & 73.7 & 81.74 & 58.37 & 70.46 & 48.39 & 64.57 & 49.48 & 64.52 \\ LwF-VR & 80.35 & 86.93 & 76.47 & 84.41 & 75.57 & 81.25 & 58.84 & 70.36 & 48.69 & 63.23 & 48.88 & 63.85 \\ ZSCL & 82.4 & **87.21** & 80.03 & 86.35 & 78.67 & 85.08 & 63.89 & 73.84 & 56.44 & 70.4 & 52.93 & 66.13 \\ IT-Prompt (Softmax+Replay) & **84.12** & 84.89 & **84.84** & **86.94** & **84.76** & **85.77** & **81.0** & **83.25** & **75.54** & **80.16** & **74.72** & **79.75** \\ IT-Prompt (C-NCM) & **82.22** & 82.99 & **83.44** & **85.89** & **85.66** & **85.87** & **80.07** & **82.77** & **78.20** & **80.36** & **77.66** & **79.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall performance of CLIP-based continual learning on ImageNet-R and CUB-200 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. reducing intra-task and inter-task confusion. Furthermore, its efficient training resource utilization avoids costly rehearsal-based strategies and full model finetuning, enabling scalability to larger datasets while achieving superior continual learning performance. Notably, across various datasets and task sequences, the C-NCM classifier demonstrates performance comparable to that of the Softmax classifier with replay, even without utilizing replay. This advantage becomes particularly evident in longer task sequences, further highlighting the effectiveness of this method in conserving training resources. To further demonstrate the effectiveness and superiority of our proposed IT-Prompt, we incorporated t-SNE visualizations, comparing our proposed method with baseline methods using 10 randomly selected categories from the CIFAR-100 dataset. These visualizations reduce the high-dimensional feature space to two dimensions while preserving neighborhood relationships, further demonstrating the effectiveness and superiority of our approach. The t-SNE visualization results (refer to Figure 4) demonstrate that our proposed IT-Prompt exhibits significant advantages over baselines, including ZSCL, LwF-VR, and iCaRL. IT-Prompt generates feature representations with well-defined class boundaries, robust intra-class cohesion, and enhanced inter-class separation. Data points form compact, structurally coherent clusters, whereas competing methods display various degrees of class overlap and boundary ambiguity. This limitation is particularly pronounced in the iCaRL method, where class clusters appear dispersed and largely indistinguishable from one another. **Additional Evaluation Results** Table 3 presents the performance comparison of different continual learning methods on the CIFAR100-T10 and ImageNet-R-T10 datasets using the newly introduced FFM and F1-score metrics. The results demonstrate the superiority of our proposed IT-Prompt approach. Specifically, IT-Prompt achieves the lowest FFM across both datasets, indicating significantly reduced catastrophic forgetting compared to other methods. Simultaneously, it attains the highest F1-score, highlighting its ability to maintain robust classification performance throughout incremental learning. These results reinforce the effectiveness of IT-Prompt in continual learning scenarios.