# RPT: Learning Point Set Representation for Siamese Visual Tracking

arXiv: https://arxiv.org/abs/2008.03467 · HTML: https://mlsanity.com/paper/2008.03467/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 26 of 26)

- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Decoupling Features in Hierarchical Propagation for Video Object Segmentation](https://mlsanity.com/paper/2210.09782/) (2022)
- [Transforming Model Prediction for Tracking](https://mlsanity.com/paper/2203.11192/) (2022)
- [STMTrack: Template-free Visual Tracking with Space-time Memory Networks](https://mlsanity.com/paper/2104.00324/) (2021)
- [Transformer Tracking with Cyclic Shifting Window Attention](https://mlsanity.com/paper/2205.03806/) (2022)
- [DepthTrack : Unveiling the Power of RGBD Tracking](https://mlsanity.com/paper/2108.13962/) (2021)
- [A Distractor-Aware Memory for Visual Object Tracking with SAM2](https://mlsanity.com/paper/2411.17576/) (2024)
- [Correlation-Aware Deep Tracking](https://mlsanity.com/paper/2203.01666/) (2022)
- [FEAR: Fast, Efficient, Accurate and Robust Visual Tracker](https://mlsanity.com/paper/2112.07957/) (2021)
- [WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking](https://mlsanity.com/paper/2201.07425/) (2022)
- [Learning Tracking Representations via Dual-Branch Fully Transformer Networks](https://mlsanity.com/paper/2112.02571/) (2021)
- [Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos](https://mlsanity.com/paper/2101.02196/) (2021)
- [RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception](https://mlsanity.com/paper/2407.10876/) (2024)
- [Distractor-Aware Memory-Based Visual Object Tracking](https://mlsanity.com/paper/2509.13864/) (2025)
- [Real-Time Visual Object Tracking via Few-Shot Learning](https://mlsanity.com/paper/2103.10130/) (2021)
- [Context-aware Visual Tracking with Joint Meta-updating](https://mlsanity.com/paper/2204.01513/) (2022)
- [Towards Efficient Training with Negative Samples in Visual Tracking](https://mlsanity.com/paper/2309.02903/) (2023)
- [SENTRY: SAM2-Enhanced Neighbor-Aware and Temporally Reasoned Memory for Visual Tracking](https://mlsanity.com/paper/2606.24449/) (2026)
- [Higher Performance Visual Tracking with Dual-Modal Localization](https://mlsanity.com/paper/2103.10089/) (2021)
- [RPT++: Customized Feature Representation for Siamese Visual Tracking](https://mlsanity.com/paper/2110.12194/) (2021)
- [TAPL: Dynamic Part-based Visual Tracking via Attention-guided Part Localization](https://mlsanity.com/paper/2110.13027/) (2021)
- [Probabilistic Tracking with Deep Factors](https://mlsanity.com/paper/2112.01609/) (2021)
- [Robot Person Following in Uniform Crowd Environment](https://mlsanity.com/paper/2205.10553/) (2022)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2302.02814/) (2023)
- [RTrack: Accelerating Convergence for Visual Object Tracking via Pseudo-Boxes Exploration](https://mlsanity.com/paper/2309.13257/) (2023)
- [Dynamic Attention Mechanism in Spatiotemporal Memory Networks for Object Tracking](https://mlsanity.com/paper/2503.16768/) (2025)

## Abstract (arXiv)

While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem. In this paper, we argue that this issue is closely related to the prevalent bounding box representation, which provides only a coarse spatial extent of object. Thus an effcient visual tracking framework is proposed to accurately estimate the target state with a finer representation as a set of representative points. The point set is trained to indicate the semantically and geometrically significant positions of target region, enabling more fine-grained localization and modeling of object appearance. We further propose a multi-level aggregation strategy to obtain detailed structure information by fusing hierarchical convolution layers. Extensive experiments on several challenging benchmarks including OTB2015, VOT2018, VOT2019 and GOT-10k demonstrate that our method achieves new state-of-the-art performance while running at over 20 FPS.