# UAV Image Multi-Labeling with Data-Efficient Transformers
## 5 Discussion  
In order to show the benefits of our multi-label classification method, we compared it against several state-of-the-art methods. We mainly choose to compare it with conditional random field method (Full-ML-CRF) [9], BOVW with color and shape representation (HCB) [31], pre-trained models with different classifiers including, SVM, multi-label regression layer (MLR), radial basis function (RBF), radial basis function neural networks (RBFNN), and multi-labeling layer (ML) [31], CNN with the max-margin loss [9], and CNN-LSTM methods with attention [14]. Table 6 shows the results of the comparison on the Trento dataset. It can be seen that our approach achieves the highest values on three metrics, namely, the specificity, the precision, and the mean average precision, with the values of 97.57%, 82.98%, and 61.38%, respectively. Furthermore, our model showed a Hamming loss of 6.75, which was lower than other methods. However, in terms of average metric, our approach achieved the second-best results, which was only 0.64% lower than the next best method, and achieved the second-lowest ranking loss. However, in terms of recall, the achieved result was low compared to other techniques. The comparative results on the Civezzano dataset are shown in Table 7. As can be seen, our method achieved the best results with a specificity of 97.91%, precision of 86.43%, average of 87.34%, and mean average precision of 69.79%, 5.20 Hamming loss, and a 15.42 ranking loss. Our method outperformed all the existing UAV multi-label classification approaches in all metrics except Recall, in which it achieved the second-best result, which was only 1.53% lower compared to the Deep Attention CNN-LSTM [14]. Figure 7: Attention heat maps generated by the first, sixth, and last layers of the encoder for classifying different samples from Civezzano multi-label datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Method** & **Sp** & **Re** & **Pr** & **F1** & **F2** & **Avg** & **mAP** & **HL** & **RL** \\ \hline Full-ML-CRF [9] & 82.20 & 70.30 & & & & 76.20 & & \\ \hline HCB [31] & 92.20 & 60.70 & & & & 76.40 & & \\ \hline GoogLeNet (MLR) & 92.70 & 60.80 & & & & 76.80 & & \\ \hline AlexNet (MLR) & 94.50 & 60.40 & & & & 77.50 & & \\ \hline GoogLeNet (RBFNN) & 95.40 & 63.10 & & & 79.30 & & \\ \hline AlexNet (RBFNN) & 96.20 & 60.60 & & & 78.40 & & \\ \hline GoogLeNet-SVM (linear) & 96.40 & 59.00 & & & 77.70 & & \\ \hline GoogLeNet-SVM(RBF) & 96.30 & 58.60 & & & 77.50 & & \\ \hline AlexNet-SVM (linear) & 95.50 & 54.50 & & & 75.00 & & \\ \hline AlexNet-SVM(RBF) & 95.70 & 52.50 & & & 74.10 & & \\ \hline GoogLeNet-RBFNN(ML) & 90.30 & 75.10 & & & 82.70 & & \\ \hline AlexNet- RBFNN(ML) & 93.00 & 70.50 & & & 81.80 & & \\ \hline CNN-RBFNN [11] & 92.60 & 68.60 & & & 80.60 & & \\ \hline CNN-MaxMargin loss [35] & 95.44 & 61.32 & 71.02 & & 78.38 & 49.50 & 9.80 & 35.72 \\ \hline CNN-Softmax loss & 92.04 & 69.49 & 61.37 & & 80.77 & 47.34 & 11.42 & 29.89 \\ \hline CNN-LSTM & 93.81 & 64.19 & 65.36 & & 79.00 & 47.46 & 10.74 & 33.62 \\ \hline LSTM+CNN +Spatial Attention & 93.86 & 66.49 & 66.43 & & 80.17 & 49.27 & 10.30 & 31.69 \\ \hline Deep Attention CNN-LSTM [14] & 94.01 & 73.17 & 68.98 & & 83.59 & 54.60 & 9.19 & 25.33 \\ \hline
**Our approach** & _97.57_ & 68.33 & 82.98 & 74.95 & 79.57 & 82.95 & 61.38 & 6.75 & 28.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the Trento multi-label dataset.