# Joint Visual Grounding and Tracking with Natural Language Specification

arXiv: https://arxiv.org/abs/2303.12027 · HTML: https://mlsanity.com/paper/2303.12027/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 46 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [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)
- [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)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [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)
- [A Fast and Accurate One-Stage Approach to Visual Grounding](https://mlsanity.com/paper/1908.06354/) (2019)
- [Distractor-aware Siamese Networks for Visual Object Tracking](https://mlsanity.com/paper/1808.06048/) (2018)
- [Learning to Assemble Neural Module Tree Networks for Visual Grounding](https://mlsanity.com/paper/1812.03299/) (2018)
- [Improving One-stage Visual Grounding by Recursive Sub-query Construction](https://mlsanity.com/paper/2008.01059/) (2020)
- [Learning Target Candidate Association to Keep Track of What Not to Track](https://mlsanity.com/paper/2103.16556/) (2021)
- [Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark](https://mlsanity.com/paper/2103.16746/) (2021)
- [SwinTrack: A Simple and Strong Baseline for Transformer Tracking](https://mlsanity.com/paper/2112.00995/) (2021)
- [Transforming Model Prediction for Tracking](https://mlsanity.com/paper/2203.11192/) (2022)
- [Transformer Tracking with Cyclic Shifting Window Attention](https://mlsanity.com/paper/2205.03806/) (2022)

## Built on by (most cited first, 30 of 59)

- [OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning](https://mlsanity.com/paper/2403.09634/) (2024)
- [Towards Unified Token Learning for Vision-Language Tracking](https://mlsanity.com/paper/2308.14103/) (2023)
- [Context-Guided Spatio-Temporal Video Grounding](https://mlsanity.com/paper/2401.01578/) (2024)
- [SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2403.16002/) (2024)
- [Tracking with Human-Intent Reasoning](https://mlsanity.com/paper/2312.17448/) (2023)
- [iKUN: Speak to Trackers without Retraining](https://mlsanity.com/paper/2312.16245/) (2023)
- [All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment](https://mlsanity.com/paper/2307.03373/) (2023)
- [Unifying Visual and Vision-Language Tracking via Contrastive Learning](https://mlsanity.com/paper/2401.11228/) (2024)
- [VastTrack: Vast Category Visual Object Tracking](https://mlsanity.com/paper/2403.03493/) (2024)
- [DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM](https://mlsanity.com/paper/2405.12139/) (2024)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding](https://mlsanity.com/paper/2502.11168/) (2025)
- [WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark](https://mlsanity.com/paper/2405.19818/) (2024)
- [Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding](https://mlsanity.com/paper/2404.06798/) (2024)
- [RefAV: Towards Planning-Centric Scenario Mining](https://mlsanity.com/paper/2505.20981/) (2025)
- [DTVLT: A Multi-modal Diverse Text Benchmark for Visual Language Tracking Based on LLM](https://mlsanity.com/paper/2410.02492/) (2024)
- [OVTR: End-to-End Open-Vocabulary Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2503.10616/) (2025)
- [Visual Language Tracking with Multi-modal Interaction: A Robust Benchmark](https://mlsanity.com/paper/2409.08887/) (2024)
- [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)
- [Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking](https://mlsanity.com/paper/2311.17085/) (2023)
- [ReasoningTrack: Chain-of-Thought Reasoning for Long-term Vision-Language Tracking](https://mlsanity.com/paper/2508.05221/) (2025)
- [Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues](https://mlsanity.com/paper/2412.19648/) (2024)
- [Robust 3D Tracking with Quality-Aware Shape Completion](https://mlsanity.com/paper/2312.10608/) (2023)
- [MLS-Track: Multilevel Semantic Interaction in RMOT](https://mlsanity.com/paper/2404.12031/) (2024)
- [Cognitive Disentanglement for Referring Multi-Object Tracking](https://mlsanity.com/paper/2503.11496/) (2025)
- [MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking](https://mlsanity.com/paper/2411.15761/) (2024)
- [FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking](https://mlsanity.com/paper/2504.13604/) (2025)
- [MEX: Memory-efficient Approach to Referring Multi-Object Tracking](https://mlsanity.com/paper/2502.13875/) (2025)

## Abstract (arXiv)

Tracking by natural language specification aims to locate the referred target in a sequence based on the natural language description. Existing algorithms solve this issue in two steps, visual grounding and tracking, and accordingly deploy the separated grounding model and tracking model to implement these two steps, respectively. Such a separated framework overlooks the link between visual grounding and tracking, which is that the natural language descriptions provide global semantic cues for localizing the target for both two steps. Besides, the separated framework can hardly be trained end-to-end. To handle these issues, we propose a joint visual grounding and tracking framework, which reformulates grounding and tracking as a unified task: localizing the referred target based on the given visual-language references. Specifically, we propose a multi-source relation modeling module to effectively build the relation between the visual-language references and the test image. In addition, we design a temporal modeling module to provide a temporal clue with the guidance of the global semantic information for our model, which effectively improves the adaptability to the appearance variations of the target. Extensive experimental results on TNL2K, LaSOT, OTB99, and RefCOCOg demonstrate that our method performs favorably against state-of-the-art algorithms for both tracking and grounding. Code is available at https://github.com/lizhou-cs/JointNLT.