# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 4 Conclusions  
Focusing on the issues of low transfer efficiency, catastrophic forgetting of pre-trained knowledge, and weak multi-modal correlation modeling in traditional full-parameter fine-tuning, an efficient multi-modal prompt vision tracker is proposed to achieve efficient prompt learning on the basis of an RGB pre-trained model. The proposed model can minimize the cost of fine-tuning downstream tasks while achieving higher multi-modal tracking performance. The model mainly includes three key components: a decoupled input enhancement module, a dynamic adaptive prompt fusion module, and a fully connected head network module. The decoupled input enhancement module enhances input from position and type embedding. The dynamic adaptive prompt fusion module achieves efficient prompt learning and multi-modal interaction through scaling convolution and low-rank cross-modal attention learning. The fully connected head network module overcomes the shortcomings of traditional convolutional head networks, such as inductive biases. The performance of MPVT was evaluated under three multi-modal scenarios: RGB-T, RGB-D, and RGB-E. The experimental results show that the model achieved the best performance on all three multi-modal datasets, achieving tracking precisions of 72.4%, 63.1%, and 76.1%, respectively, and outperforming the second-best model by 2.2%, 0.9%, and 0.3%, respectively. Further ablation studies showed that all three proposed components contribute to more accurate target tracking. Furthermore, the model achieved efficient multi-modal transfer with less than 1% of learnable parameters. The efficiency analysis showed that MPVT is much more effective in training time and GPU memory usage than full-parameter fine-tuning models, saving up to 43.8% of GPU memory and 62.9% of training time. Recently, many researchers have fused vision and text in various large models [44]. Extending MPVT to vision-language tracking requires solving the fundamental challenge of spatial-semantic alignment, i.e., bridging textual descriptions with visual coordinates while maintaining real-time efficiency. Key research directions include the following: (1) developing geometric-aware attention mechanisms to ground linguistic concepts (e.g., \"left\", \"occluded\") to pixel-level locations; (2) designing memory-augmented architectures that preserve object identity across evolving text descriptions; and (3) designing efficient fusion schemes to avoid computational explosion when combining visual tokens with linguistic tokens. In future work, we will introduce lingual information into MPVT to further extend multi-modal scenarios. Conceptualization, J.X. and D.C.; methodology, J.X., Y.F. ([PERSON]), and J.Z. ; software, J.X. and T.H. ; validation, T.H., X.W. and Y.F. ([PERSON]); formal analysis, J.X., T.H. and X.W. ; investigation, J.X., Y.F. ([PERSON]) and D.C.; resources, X.W. and Y.F. ([PERSON]); data curation, X.W. and Y.F. ([PERSON]); writing--original draft preparation, J.X. and D.C.; writing--review and editing, J.X., J.Z., Y.F. ([PERSON]), X.W. and D.C.; visualization, T.H. and X.W. ; supervision, J.Z. and D.C.; funding acquisition, Y.F. ([PERSON]) and D.C. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Major Science and Technology Projects in Sichuan Province under Grant No. 2024 ZDZX0021 and the Major Program of National Natural Science Foundation of China under Grant No. T2293771. Not applicable Data Availability Statement: Three datasets used in this paper are available from public URLs.