# Explicit Visual Prompts for Visual Object Tracking

arXiv: https://arxiv.org/abs/2401.03142 · HTML: https://mlsanity.com/paper/2401.03142/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020)
- [Ocean: Object-aware Anchor-free Tracking](https://mlsanity.com/paper/2006.10721/) (2020)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [Siamese Box Adaptive Network for Visual Tracking](https://mlsanity.com/paper/2003.06761/) (2020)
- [AiATrack: Attention in Attention for Transformer Visual Tracking](https://mlsanity.com/paper/2207.09603/) (2022)
- [LaSOT: A High-quality Large-scale Single Object Tracking Benchmark](https://mlsanity.com/paper/2009.03465/) (2020)
- [STMTrack: Template-free Visual Tracking with Space-time Memory Networks](https://mlsanity.com/paper/2104.00324/) (2021)
- [Learning the Model Update for Siamese Trackers](https://mlsanity.com/paper/1908.00855/) (2019)
- [TCTrack: Temporal Contexts for Aerial Tracking](https://mlsanity.com/paper/2203.01885/) (2022)
- [Learn to Match: Automatic Matching Network Design for Visual Tracking](https://mlsanity.com/paper/2108.00803/) (2021)

## Built on by (most cited first, 30 of 50)

- [SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory](https://mlsanity.com/paper/2411.11922/) (2024)
- [Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking](https://mlsanity.com/paper/2503.06625/) (2025)
- [Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking](https://mlsanity.com/paper/2504.09228/) (2025)
- [MambaEVT: Event Stream based Visual Object Tracking using State Space Model](https://mlsanity.com/paper/2408.10487/) (2024)
- [M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension](https://mlsanity.com/paper/2407.01131/) (2024)
- [MambaLCT: Boosting Tracking via Long-term Context State Space Model](https://mlsanity.com/paper/2412.13615/) (2024)
- [Adaptive Perception for Unified Visual Multi-modal Object Tracking](https://mlsanity.com/paper/2502.06583/) (2025)
- [UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network](https://mlsanity.com/paper/2503.12888/) (2025)
- [How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language in Vision-Language Tracking](https://mlsanity.com/paper/2411.15600/) (2024)
- [CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features](https://mlsanity.com/paper/2505.19434/) (2025)
- [ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking](https://mlsanity.com/paper/2507.19875/) (2025)
- [Improving Visual Object Tracking through Visual Prompting](https://mlsanity.com/paper/2409.18901/) (2024)
- [ReasoningTrack: Chain-of-Thought Reasoning for Long-term Vision-Language Tracking](https://mlsanity.com/paper/2508.05221/) (2025)
- [Long-Term Visual Object Tracking with Event Cameras: An Associative Memory Augmented Tracker and A Benchmark Dataset](https://mlsanity.com/paper/2403.05839/) (2024)
- [SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding](https://mlsanity.com/paper/2502.16786/) (2025)
- [HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking](https://mlsanity.com/paper/2507.07603/) (2025)
- [MITracker: Multi-View Integration for Visual Object Tracking](https://mlsanity.com/paper/2502.20111/) (2025)
- [SAMITE: Position Prompted SAM2 with Calibrated Memory for Visual Object Tracking](https://mlsanity.com/paper/2507.21732/) (2025)
- [Tracking Transforming Objects: A Benchmark](https://mlsanity.com/paper/2404.18143/) (2024)
- [XS-VID: A Large-Scale Benchmark for Small Object Detection and Tracking in Videos](https://mlsanity.com/paper/2407.18137/) (2024)
- [Explicit Context Reasoning with Supervision for Visual Tracking](https://mlsanity.com/paper/2507.16191/) (2025)
- [VPTracker: Global Vision-Language Tracking via Visual Prompt](https://mlsanity.com/paper/2512.22799/) (2025)
- [SEATrack: Simple, Efficient, and Adaptive Multimodal Tracker](https://mlsanity.com/paper/2604.12502/) (2026)
- [Visual Object Tracking across Diverse Data Modalities: A Review](https://mlsanity.com/paper/2412.09991/) (2024)
- [Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking](https://mlsanity.com/paper/2506.23783/) (2025)
- [UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions](https://mlsanity.com/paper/2507.00648/) (2025)
- [FARTrack: Fast Autoregressive Visual Tracking with High Performance](https://mlsanity.com/paper/2602.03214/) (2026)
- [UETrack: A Unified and Efficient Framework for Single Object Tracking](https://mlsanity.com/paper/2603.01412/) (2026)
- [Learning Motion Blur Robust Vision Transformers for Real-Time UAV Tracking](https://mlsanity.com/paper/2407.05383/) (2024)
- [Learning an Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking](https://mlsanity.com/paper/2412.20002/) (2024)

## Abstract (arXiv)

How to effectively exploit spatio-temporal information is crucial to capture target appearance changes in visual tracking. However, most deep learning-based trackers mainly focus on designing a complicated appearance model or template updating strategy, while lacking the exploitation of context between consecutive frames and thus entailing the textitwhen-and-how-to-update dilemma. To address these issues, we propose a novel explicit visual prompts framework for visual tracking, dubbed EVPTrack. Specifically, we utilize spatio-temporal tokens to propagate information between consecutive frames without focusing on updating templates. As a result, we cannot only alleviate the challenge of textitwhen-to-update, but also avoid the hyper-parameters associated with updating strategies. Then, we utilize the spatio-temporal tokens to generate explicit visual prompts that facilitate inference in the current frame. The prompts are fed into a transformer encoder together with the image tokens without additional processing. Consequently, the efficiency of our model is improved by avoiding textithow-to-update. In addition, we consider multi-scale information as explicit visual prompts, providing multiscale template features to enhance the EVPTrack's ability to handle target scale changes. Extensive experimental results on six benchmarks (i.e., LaSOT, LaSOTrm ext, GOT-10k, UAV123, TrackingNet, and TNL2K.) validate that our EVPTrack can achieve competitive performance at a real-time speed by effectively exploiting both spatio-temporal and multi-scale information. Code and models are available at https://github.com/GXNU-ZhongLab/EVPTrack.