# Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
## 4 Experiments
### Performance  
#### 4.3.1 Comparison with Baseline  
We compared our model with three competitive baselines. We constructed baselines as follows: **YOLO-joint** jointly trains the YOLOv4 detector on the base and novel classes, and uses the identical number of iterations as Meta-YOLO to train this baseline model. **YOLO-based** takes the same training strategy in Meta-YOLO. **YOLO-ft** applies the same training strategy of ours but trains the detector to fully converge. **TT100K** The K-shot traffic sign detection is performed based on K = (1, 2, 3, 5, 10) across three novel/base class splits. As shown in the experimental evaluation results in Table 2, our proposed model significantly outperforms the baseline. It reveals the generalization weakness of the one-stage detector YOLOv4 with a small number of labels in the training term. Comparing the performance results of the YOLO-joint and YOLO-based baselines shows that joint training biases the detection results toward the base class and nothing about the new class. Note that, YOLO-ft performs significantly better than the other two baselines, which proves the necessity of a two-stage strategy for a meta-learner. Furthermore, the attention maps are visualized in Figure 5. The attention maps show that these highlighted regions are almost correlated with traffic sign instances, which indicates that the feature decorrelation module effectively removes irrelevant features and enhances the generalization of meta-learned representations. **GTSDB**: We evaluated 10-shot/30-shot setups on the GTSDB [4] benchmark and report the standard GTSDB metrics. The results on the novel classes are shown in Table 3. In both cases, meta-YOLO outperforms the other baselines. The results show that the performance of the baselines in detecting new classes is extremely poor, which is caused by the unbalanced setting of the number of base classes and new classes, further revealing the vulnerability of YOLO to the generalization problem. In Figure 6, some comparisons between YOLO-ft and Meta-YOLO are visualized when detecting novel class traffic signs; the results indicate our method is indeed effective. **GTSDB to TT100K:** In this cross-dataset few-shot traffic sign detection setup, all the baselines are trained with 10-shot objects in novel classes on GTSDB, while they are evaluated on the TT100K test set. Distinct from the previous experiments that concentrate on evaluating cross-category model generalization, this setup goes further to manifest the cross-domain generalization ability. The mAP of YOLO-based and YOLO-ft achieve the detection performance of 20.1% and 32.6%, respectively. Instead, Meta-YOLO achieves 39.8%, while this performance is poorer than when using base classes in TT100K (which has a mAP of around 42%). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Novel Set 1**} & \multicolumn{6}{c}{**Novel Set 2**} & \multicolumn{6}{c}{**Novel Set 3**} \\ \hline
**Method/Shot** & **1** & **2** & **3** & **5** & **10** & **1** & **2** & **3** & **5** & **10** & **1** & **2** & **3** & **5** & **10** \\ \hline YOLO-joint & 0.0 & 0.8 & 1.9 & 4.3 & 6.2 & 0.3 & 0.8 & 5.5 & 7.3 & 12.4 & 0.0 & 0.9 & 2.6 & 5.3 & 8.4 \\ YOLO-based & 2.4 & 7.4 & 11.2 & 19.0 & 20.5 & 3.2 & 5.8 & 19.6 & 27.2 & 28.6 & 4.3 & 5.2 & 10.8 & 21.7 & 22.7 \\ YOLO-ft & 10.5 & 14.6 & 18.3 & 28.8 & 30.2 & 11.0 & 18.4 & 26.7 & 33.5 & 35.0 & 7.4 & 12.1 & 20.2 & 30.6 & 37.2 \\ \hline Ours & **18.3** & **21.7** & **26.8** & **30.0** & **45.3** & **19.7** & **22.9** & **29.3** & **37.5** & **42.1** & **15.6** & **19.9** & **29.7** & **39.4** & **45.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Few-shot detection mAP on TT100K test set for novel classes. The baselines are evaluated using three separate sets of novel classes. Figure 5: Visualization of attention maps. With feature decorrelation module introduced, clear responses for classes are observed, manifesting this module’s effectiveness in promoting generalization of meta-learned representations.