# UTPTrack: Towards Simple and Unified Token Pruning for Visual Tracking

arXiv: https://arxiv.org/abs/2602.23734 · HTML: https://mlsanity.com/paper/2602.23734/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Visual token pruning in multimodal models, one of 12 directions in Efficient LLM inference and serving.
- First posted on arXiv 27 Feb 2026.

## Placed in

- Efficient LLM inference and serving / [Visual token pruning in multimodal models](https://mlsanity.com/inference/visual-token-pruning-in-multimodal-models/)

## Built on by (most cited first, 9 of 9)

- [Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy](https://mlsanity.com/paper/2606.09080/) (2026)
- [WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning](https://mlsanity.com/paper/2607.28418/) (2026)
- [MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs](https://mlsanity.com/paper/2610.01434/) (2026)
- [Dynamic Pondering Sparsity-aware Mixture-of-Experts Transformer for Event Stream based Visual Object Tracking](https://mlsanity.com/paper/2605.06112/) (2026)
- [Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking](https://mlsanity.com/paper/2606.26455/) (2026)
- [LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking](https://mlsanity.com/paper/2609.33306/) (2026)
- [ACPruner: Visual Token Pruning as Biased Attention Coverage Maximization in LVLMs](https://mlsanity.com/paper/2609.34558/) (2026)
- [UniMergeTrack: Hierarchical Model Merging for General RGB-X Visual Object Tracking](https://mlsanity.com/paper/2610.13053/) (2026)
- [Bifocal: Training-Free Mixed Granularity Visual Token Pruning for Multimodal LLMs](https://mlsanity.com/paper/2610.18399/) (2026)

## Abstract (arXiv)

One-stream Transformer-based trackers achieve advanced performance in visual object tracking but suffer from significant computational overhead that hinders real-time deployment. While token pruning offers a path to efficiency, existing methods are fragmented. They typically prune the search region, dynamic template, and static template in isolation, overlooking critical inter-component dependencies, which yields suboptimal pruning and degraded accuracy. To address this, we introduce UTPTrack, a simple and Unified Token Pruning framework that, for the first time, jointly compresses all three components. UTPTrack employs an attention-guided, token type-aware strategy to holistically model redundancy, a design that seamlessly supports unified tracking across multimodal and language-guided tasks within a single model. Extensive evaluations on 10 benchmarks demonstrate that UTPTrack achieves a new state-of-the-art in the accuracy-efficiency trade-off for pruning-based trackers, pruning 65.4% of vision tokens in RGB-based tracking and 67.5% in unified tracking while preserving 99.7% and 100.5% of baseline performance, respectively. This strong performance across both RGB and multimodal scenarios underlines its potential as a robust foundation for future research in efficient visual tracking. Code will be released at https://github.com/EIT-NLP/UTPTrack.