## 6 Discussion  
In this section, we explain the results of the experiments, and discuss the practical applications of our research, as well as the future work.  
Generally speaking, the experiments show the NV-softmax loss works and performs better than the original softmax loss and other softmax-based loss such as the A-softmax and V-softmax. Comparing with using classification loss alone, the joint use of triplet loss can improve the model's performance. In practical application, the return list of our method is intuitively better.  
Furthermore, we observe an interesting thing in the ablation experiments that the boosting effect of NV-softmax loss on Rank-5 and Rank-10 is greater than that on Rank-1 (Tables 1-4). This shows that for some hard samples, our method can make the score of the corresponding class of the ground truth as high as possible even if cannot classify the hard samples accurately, thus improving the returned results in top 5 or top 10 list. This point is also proven in the visualization experiments. In the practical application of vehicle ReID, the ranking list of the top 10 returned by the network is often checked manually by people to improve the accuracy. Our method improves the Rank-10 in the experiments greatly, thus has an important significance in practical applications.  
Although our approach achieves excellent performance on most datasets and metrics, we observe that the Rank-1 of our model is lower than that of A-softmax on VehicleID dataset (Table 7). In theory, A-softmax compresses the region of each class in the feature space to 1/m of the original by mathematical transformation, which is a global hard constraint and thus achieve good performance. However, the computational process of A-softmax includes the multiplicative angle formula, which greatly increases the computational complexity. Considering that the advantage of A-softmax is not significant, our model with negligible additional computational complexity has better application prospects.  
Improving the accuracy of the ReID algorithm is critical to help security departments accurately query, locate and track the suspect vehicles, and there is no doubt that our method achieves this goal. We are going to publish our dataset to facilitate the study on UAV-based vehicle ReID. Furthermore, we have integrated our approach into the FastReID framework [30], which is a practical product-level library. In the near future, we will deploy the code to UAVs for practical use.  
Despite the good performance comparing with the other modified softmax loss, we know there are still some improvements that can be made. Firstly, our dataset is relatively small. Considering that some of the material we shot has not been fully labeled, we will continue to update our self-built dataset in the future. Secondly, the normalized virtual softmax loss is achieved by dynamically inserting virtual classes into the FC layer, which does not affect the main structure of the network. In the future, we hope to try to use the normalized virtual softmax loss in more advanced feature extraction networks, such as multiple granularities network (MGN) [37], recurrent-neural-network-based hierarchical attention model (RNN-HA) [38] and region-aware model (RAM) [39].