# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 1 Introduction  
Visual target tracking is widely used in various scenarios, such as video surveillance and autonomous driving. Target tracking based on RGB images [1, 2, 3] has high performance due to its large-scale training data. However, such images often make tracking fail under poor lighting conditions, such as darkness, cluttered backgrounds, and high-speed motion. With the development of multi-modal technology, auxiliary modalities can enhance the success rate, precision, and robustness of tracking by utilizing the complementarity between modalities. Some classical scenarios include RGB-T tracking [4, 5, 6], RGB-D tracking [7, 8], and RGB-E tracking [9, 10, 11]. For RGB-T tracking, some researchers have proposed fusion methods based on feature decoupling. [PERSON] et al. [12] constructed a multi-branch network to handle RGB and thermal infrared images separately. ADRNet [13] further utilizes adaptive universal branches for decoupling. MANet [14] jointly learns shared, modal-specific, and instance-aware features through universal, modal, and instance adapters. MANet++ [15] incorporates a hierarchical difference loss to maximize the distribution difference between modal-specific and shared features. DMCNet [16] adopts a dual-door control mutual condition module to suppress noise through cross-modal feature guidance. With the deepening development of attention mechanisms, some researchers have proposed dynamic fusion methods based on feature selection. For example, FANet [17] involves a hierarchical feature aggregation module to adaptively fuse multi-layer features. CMPP [18] includes a cross-modal propagation framework that achieves complementarity through spatio-temporal domain feature diffusion. ViPT [19] uses infrared modalities as visual prompts for visible modalities and utilizes the attention mechanism to achieve cross-modal interaction. In addition, some methods incorporate the unique characteristics of Transformer models to design multi-modal interaction modules for RGB-T target tracking. For example, MTNet [20] utilizes channel aggregation and distribution modules to eliminate redundancy. SiamCAF [21] uses complementary coupling modules to enhance feature similarity through cross-connections. Very recently, [PERSON] et al. [22] proposed a novel lightweight adapter to boost fusion performance for RGB-T target tracking. Traditional RGB-D target tracking methods mainly adopt manually designed features and shallow fusion strategies. For example, DS-KCF [23] achieves bimodal feature-level fusion through HOG feature extraction, while STC [24] improves tracking robustness through decision-level fusion. With the development of deep learning, researchers have begun to explore deep feature fusion. For example, [PERSON] et al. [25] proposed a dual-branch CNN architecture, which extracts RGB and deep features separately and enhances discriminability through weighted fusion. OTR [26] introduces deep dynamic spatial constraints to optimize DCF (discriminative correlation filter) learning, significantly improving tracking stability in occluded scenarios. In recent years, researchers have focused more on cross-modal interaction modeling to fully utilize the complementary information of multiple modalities. DeT [7] incorporates a symmetrical double-branch structure and achieves early fusion through feature concatenation. Recently, [PERSON] et al. [27] proposed a new RGB-D approach to capture the interactive information between modalities. The RGB-E tracking method [28] often uses a cross-modal attention mechanism for feature fusion. AFNet [10] utilizes the high temporal resolution of event data to design a cross-frame rate alignment module, achieving a tracking frequency of 1000 Hz. HRCEU-Track [11] introduces a Mask Autoencoder (MAE) to randomly mask RGB and event tokens, enhances cross-modal representation consistency through reconstruction tasks, and combines orthogonal high-rank regularization to suppress network fluctuations.