# Transformer Tracking with Cyclic Shifting Window Attention

arXiv: https://arxiv.org/abs/2205.03806 · HTML: https://mlsanity.com/paper/2205.03806/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 51 references that are in the map.
- First posted on arXiv 8 May 2022.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [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)
- [MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://mlsanity.com/paper/2012.00759/) (2020)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [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)
- [Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking](https://mlsanity.com/paper/2103.11681/) (2021)
- [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)
- [Distractor-aware Siamese Networks for Visual Object Tracking](https://mlsanity.com/paper/1808.06048/) (2018)
- [COTR: Correspondence Transformer for Matching Across Images](https://mlsanity.com/paper/2103.14167/) (2021)
- [SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines](https://mlsanity.com/paper/1911.06188/) (2019)
- [Siam R-CNN: Visual Tracking by Re-Detection](https://mlsanity.com/paper/1911.12836/) (2019)
- [Deeper and Wider Siamese Networks for Real-Time Visual Tracking](https://mlsanity.com/paper/1901.01660/) (2019)
- [Know Your Surroundings: Exploiting Scene Information for Object Tracking](https://mlsanity.com/paper/2003.11014/) (2020)

## Built on by (most cited first, 30 of 75)

- [Generalized Relation Modeling for Transformer Tracking](https://mlsanity.com/paper/2303.16580/) (2023)
- [Joint Visual Grounding and Tracking with Natural Language Specification](https://mlsanity.com/paper/2303.12027/) (2023)
- [MixFormerV2: Efficient Fully Transformer Tracking](https://mlsanity.com/paper/2305.15896/) (2023)
- [Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking](https://mlsanity.com/paper/2308.06904/) (2023)
- [Compact Transformer Tracker with Correlative Masked Modeling](https://mlsanity.com/paper/2301.10938/) (2023)
- [POSTA: A Go-to Framework for Customized Artistic Poster Generation](https://mlsanity.com/paper/2503.14908/) (2025)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [Learning Spatial-Frequency Transformer for Visual Object Tracking](https://mlsanity.com/paper/2208.08829/) (2022)
- [LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing](https://mlsanity.com/paper/2507.00029/) (2025)
- [Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.19386/) (2026)
- [Autogenic Language Embedding for Coherent Point Tracking](https://mlsanity.com/paper/2407.20730/) (2024)
- [ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.20358/) (2026)
- [LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking](https://mlsanity.com/paper/2309.09249/) (2023)
- [TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval](https://mlsanity.com/paper/2604.21806/) (2026)
- [PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning](https://mlsanity.com/paper/2504.16023/) (2025)
- [MVP: Winning Solution to SMP Challenge 2025 Video Track](https://mlsanity.com/paper/2507.00950/) (2025)
- [Reading Relevant Feature from Global Representation Memory for Visual Object Tracking](https://mlsanity.com/paper/2402.14392/) (2024)
- [Correlational Image Modeling for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2303.12670/) (2023)
- [Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement](https://mlsanity.com/paper/2308.15816/) (2023)
- [Transformers in Single Object Tracking: An Experimental Survey](https://mlsanity.com/paper/2302.11867/) (2023)
- [Correlation-Embedded Transformer Tracking: A Single-Branch Framework](https://mlsanity.com/paper/2401.12743/) (2024)
- [Improving Visual Object Tracking through Visual Prompting](https://mlsanity.com/paper/2409.18901/) (2024)
- [CST Anti-UAV: A Thermal Infrared Benchmark for Tiny UAV Tracking in Complex Scenes](https://mlsanity.com/paper/2507.23473/) (2025)
- [Exploring Dynamic Transformer for Efficient Object Tracking](https://mlsanity.com/paper/2403.17651/) (2024)
- [RGB-Sonar Tracking Benchmark and Spatial Cross-Attention Transformer Tracker](https://mlsanity.com/paper/2406.07189/) (2024)
- [Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation](https://mlsanity.com/paper/2308.13266/) (2023)
- [SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking](https://mlsanity.com/paper/2602.23963/) (2026)
- [Seeing Further and Wider: Joint Spatio-Temporal Enlargement for Micro-Video Popularity Prediction](https://mlsanity.com/paper/2604.20311/) (2026)
- [GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss](https://mlsanity.com/paper/2304.00242/) (2023)
- [Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights](https://mlsanity.com/paper/2403.19882/) (2024)

## Abstract (arXiv)

Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new transformer architecture with multi-scale cyclic shifting window attention for visual object tracking, elevating the attention from pixel to window level. The cross-window multi-scale attention has the advantage of aggregating attention at different scales and generates the best fine-scale match for the target object. Furthermore, the cyclic shifting strategy brings greater accuracy by expanding the window samples with positional information, and at the same time saves huge amounts of computational power by removing redundant calculations. Extensive experiments demonstrate the superior performance of our method, which also sets the new state-of-the-art records on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks.