# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 3 Experimental Results and Discussion
### Main Results  
Various methods were compared with MPVT using three datasets, including TBSI [4], APFNet [5], FANet [17], CAT [12], MDNet [36], PrDIMP50 [37], LTMU [38], OSN Track [32], TransT [39], DAL [8], TATrack [40], TABBTrack [41], ProTrack [29], ViPT [19], and Un-Track [42]. For the RGB-T scenario, various types of RGB-T tracking models were compared with MPVT. Early methods such as FANet [5] mainly improved the complementarity of multi-modal information through feature fusion. Feature decoupling-based methods such as CAT [12] and APFNet [5] introduced difficult attribute branches and progressive fusion mechanisms, respectively. TransT [39] applied Transformer to visual tracking, significantly \begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Value** \\ \hline Layers (\(N\)) & 40 \\ Hidden dimensions (\(d\)) & 1536 \\ Size of searching image (\(H_{x}=W_{x}\)) & 378 \\ Size of template image (\(H_{z}=W_{z}\)) & 196 \\ Feature size of searching region & 27 \\ Feature size of template region & 14 \\ Epochs & 60 \\ Batch size & 16 \\ Learning rate & Maximal rate was \(10^{-4}\); minimal rate was \(10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 1: The model parameter settings. enhancing the global information interaction between templates and searching regions. OSTrack [32] and TBSI [4] further optimized the Transformer architecture, achieving significant improvements in precision and generalization performance. ProTrack [29] utilized a multi-modal prompt tracking framework, and ViPT [19] further fine-tuned the pre-trained model through visual prompts. We conducted extensive comparisons based on the various methods mentioned above, and the experimental results are shown in the third and fourth columns of Table 2. MPVT achieved the best performance among all models, with a success rate of 0.578 and a precision of 0.724. Compared with the second best model, TATrack [40], it increased the success rate and precision by 1.9% and 2.2%, respectively, and compared with ViPT [19], it increased the success rate and precision by 5.3% and 7.3%, respectively. For the RGB-D scenario, to conduct a comprehensive comparison, various models were introduced as baselines. Besides the methods used in the RGB-T scenario, some other methods were also compared with MPVT. DAL [8] constructed a long-term tracking framework for deep perception through an end-to-end deep feature fusion network. TABBTrack [41] adopted a three-stream architecture and combined temporal features to fuse depth images. The results are shown in columns 5-7 in Table 2. MPVT achieved the highest performance among all the compared models, with an \(F1\)-score of 0.628, a tracking recall rate of 0.626, and a precision of 0.631. Compared with ViPT [19], it increased the \(F1\)-score, \(TRe\), and \(Pr\) by 3.4%, 3%, and 3.0%, respectively, and compared with the second-best model, TABTrack [41], it increased the \(F1\)-score, \(TRe\), and \(Pr\) by 1%, 1.1%, and 0.9%, respectively. Among all the compared models in the RGB-E scenario, MPVT achieved the highest tracking success rate (0.624) and precision (0.761), with improvements of over 3.2% and 0.3%, respectively, compared with those of the second-best model, ViPT [19], in the RGB-E scenario, as shown in the eighth and ninth columns of Table 2.  
Regarding degraded scenarios, Figures 3 and 4 present precision plots for 15 degraded scenarios involving the LasHeR and VisEvent datasets, respectively (DepthTrack is excluded due to unavailable degradation labels). MPVT consistently outperformed baselines across all scenarios. The performance gain was notable under abrupt illumination variation (AIV), hyaline occlusion (HO), and background clutter (BC) in LasHeR and also under low illumination (LI), AIV, and HO in the VisEvent dataset. For instance, MPVT outperformed ViPT by 0.306 under AIV. These results demonstrate the model's robust performance in degraded conditions.