# Multimodal Framework for Long-Tailed Recognition
## 3 Experiment Details
### Main Experimental Results  
#### 3.6.1 Results on CIFAR-10-LT and CIFAR-100-LT  
Table 1 reports the Top-1 accuracy of CIFAR-10-LT and CIFAR-100-LT using ResNet-32. The multimodal long-tailed data recognition (MMLTR) method proposed in this paper  
achieved the best results for all imbalance ratios (200, 100, 50, and 20), which verified the effectiveness of the multimodal long-tailed data recognition method in this paper. The results show that the proposed method outperforms classical rebalancing methods, reflecting the effectiveness of our contrastive learning framework.  
The proposed method also outperforms the tested data augmentation methods. It also shows stronger competitiveness compared with the two-stage methods, which reflects the effectiveness of the two-stage method in this paper, namely, the contrastive learning stage and the classification learning stage. The proposed method employs a multi-branch model during the classification learning stage, and its performance is compared with various other multi-branch methods. The results show that its accuracy surpasses that of these other methods, demonstrating the effectiveness of the proposed model in classification. We also compared our framework with many recent state-of-the-art methods to demonstrate its effectiveness in multimodal long-tailed data recognition. In Table 2, the accuracy on the majority class (more than 100 training images), median class (20\(\sim\)100 training images), and minority class (fewer than 20 training images) are reported. The recognition accuracy of the MMLTR method in this study is more than 7% higher than that of the previous method for the tail category, and the overall recognition accuracy is 1.8% higher than that of the previous method. It can be seen that most of the improvement comes from the tail category.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{3}{c}{**CIFAR-10-LT**} & \multicolumn{3}{c}{**CIFAR-100-LT**} \\
**Imbalance Ratio** & **200** & **100** & **50** & **20** & **200** & **100** & **50** & **20** \\ \hline Focal loss & 65.29 & 70.38 & 76.72 & 83.23 & 35.62 & 38.69 & 44.32 & 51.95 \\ Class-Balanced & 68.89 & 74.57 & 79.27 & 84.36 & 36.23 & 39.60 & 45.32 & 52.59 \\ LDAM-DRW & - & 77.03 & - & - & - & 42.04 & - & - \\ Equalization & - & - & - & - & 43.38 & - & - & - \\ LFME & - & - & - & - & - & 43.80 & - & - \\ BBN & - & 79.82 & 82.18 & - & - & 42.56 & 47.02 & - \\ RIDE & - & - & - & - & - & 49.10 & - & - \\ Mixup & - & 73.09 & - & - & - & 40.83 & - & - \\ Remix-DRW & - & 79.76 & - & - & - & 46.77 & - & - \\ CAM & - & 80.03 & 83.59 & - & - & 47.83 & 51.69 & - \\ Meta-weight-net & 67.20 & 73.57 & 79.10 & 84.45 & 36.62 & 41.61 & 45.66 & 53.04 \\ Domain Adap. & 77.23 & 80.00 & 82.88 & 86.46 & 39.53 & 44.70 & 50.08 & 55.73 \\ LADE & - & - & - & - & - & 45.40 & 50.50 & - \\ \hline MMLTR (ours) & **78.51** & **81.52** & **85.43** & **88.02** & **47.11** & **50.92** & **53.72** & **58.92** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on the CIFAR-10-LT and CIFAR-100-LT datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Many-Shot** & **Medium-Shot** & **Few-Shot** & **All** \\ \hline Focal loss & 64.3 & 37.4 & 7.1 & 37.4 \\ CB loss & 65.0 & 37.6 & 10.3 & 38.7 \\ Remix & 69.6 & 40.7 & 8.8 & 40.9 \\ OLTR * & 61.8 & 41.4 & 17.6 & 41.2 \\ Mixup & **70.7** & 40.4 & 8.8 & 41.2 \\ LDAM-DRW & 61.5 & 41.7 & 20.2 & 42.0 \\ \(\tau\)-norm * & 65.7 & 43.6 & 17.3 & 43.2 \\ cRT * & 64.0 & 44.8 & 18.1 & 43.3 \\ RIDE * & 69.3 & 49.3 & 26.0 & 49.1 \\ \hline MMLTR (ours) & 65.8 & **52.3** & **33.6** & **50.9** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Many-shot (over 100 training images), medium-shot (20\(\sim\)100 training images), and few-shot class (fewer than 20 training images) accuracy on the CIFAR-100-LT dataset with an imbalance rate of 100. * Represents the result from RIDE [8]. Other results are from ACE [30]. (The best results are bolded).