# Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
## 4 Experiments
### Ablation Studies  
We constructed extensive ablation experiments to research how our proposed individual components contribute to the detection performance. The experiments used the TT100K base/novel split1 with 10-shot data on novel classes.  
**Effect of Feature Decorrelation Module (FDM).** We introduced FDM into the model to utilize RFF to map non-linear indivisible features of raw space to a higher dimensional space, and transform these into linearly separable features, thereby obtaining the real features of the object. As shown in Table 5 FDM is effective in hindering the reliance on category-specific features. Without FDM the method has a strong effect on the performance on novel classes, but is only slightly affected on base classes. With FDM included, the detection performance of the base and novel classes improves, which shows that the more generalizable representations are learned effectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**mAP**} & \multicolumn{2}{c}{**mAP\({}_{\text{base}}\)**} & \multicolumn{2}{c}{**mAP\({}_{\text{novel}}\)**} \\ \hline
**Method/Shot** & **5-Shot** & **10-Shot** & **5-Shot** & **10-Shot** & **5-Shot** & **10-Shot** \\ \hline [PERSON] et al. [46] & 29.2 & 38.9 & 46.4 & 56.1 & 10.8 & 12.4 \\ \hline [PERSON] et al. [16] & 32.1 & 43.4 & 50.1 & 58.6 & 10.4 & 11.7 \\ \hline [PERSON] et al. [47] & 46.1 & 51.2 & 58.4 & **68.0** & **37.8** & **42.2** \\ \hline [PERSON] et al. [48] & 46.0 & 50.2 & 57.0 & 65.4 & 35.8 & 38.3 \\ \hline Ours & **46.2** & **51.7** & **58.7** & 67.9 & 37.6 & 41.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of performance with other detectors under 10 shots.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Design Choice**} & & \\ \hline \multirow{2}{*}{**FDM**} & \multicolumn{2}{c}{**Three-Head**} & \multirow{2}{*}{\(\mathcal{L}_{\text{FDM}}\)} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**Novel**} \\  & & & & 64.9 & 43.5 \\ \hline \(\surd\) & & \(\surd\) & 65.0 & 44.2 \\ \hline  & \(\surd\) & \(\surd\) & 65.4 & 43.1 \\ \hline \(\surd\) & \(\surd\) & & 66.2 & 44.7 \\ \hline \(\surd\) & \(\surd\) & \(\surd\) & 67.9 & 45.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation studies of several design choices.  
**Effect of meta-learner.** Since Meta-YOLO is formally designed as a meta-learner, it is crucial to observe whether our method truly improves the performance. To verify our claim, we ablated the initialization term of the meta-learner to observe the object detection performances in the base and novel classes. As illustrated in Table 6, our two-stage strategy significantly boosts the model generalization and learning capability.  
**Effect of three-head mechanism.** We introduced a three-head mechanism with three different scale modules that complemented each other to predict objects within their respective specified ranges. As shown in Table 6, the three-head mechanism achieves a significant improvement in the detection performance for base classes. With a limited number of base categories, it unavoidably has poor performance on novel classes.