# Unifying Visual and Vision-Language Tracking via Contrastive Learning

arXiv: https://arxiv.org/abs/2401.11228 · HTML: https://mlsanity.com/paper/2401.11228/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 48 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 30 of 37)

- [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)
- [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)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [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)
- [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)
- [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)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [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)
- [SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines](https://mlsanity.com/paper/1911.06188/) (2019)
- [SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking](https://mlsanity.com/paper/1911.07241/) (2019)
- [Learning to Assemble Neural Module Tree Networks for Visual Grounding](https://mlsanity.com/paper/1812.03299/) (2018)
- [SeqTR: A Simple yet Universal Network for Visual Grounding](https://mlsanity.com/paper/2203.16265/) (2022)
- [Improving One-stage Visual Grounding by Recursive Sub-query Construction](https://mlsanity.com/paper/2008.01059/) (2020)
- [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017)
- [LaSOT: A High-quality Large-scale Single Object Tracking Benchmark](https://mlsanity.com/paper/2009.03465/) (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)
- [STMTrack: Template-free Visual Tracking with Space-time Memory Networks](https://mlsanity.com/paper/2104.00324/) (2021)

## Built on by (most cited first, 27 of 27)

- [WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark](https://mlsanity.com/paper/2405.19818/) (2024)
- [DTVLT: A Multi-modal Diverse Text Benchmark for Visual Language Tracking Based on LLM](https://mlsanity.com/paper/2410.02492/) (2024)
- [Animate Your Motion: Turning Still Images into Dynamic Videos](https://mlsanity.com/paper/2403.10179/) (2024)
- [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)
- [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)
- [VPTracker: Global Vision-Language Tracking via Visual Prompt](https://mlsanity.com/paper/2512.22799/) (2025)
- [Visual Object Tracking across Diverse Data Modalities: A Review](https://mlsanity.com/paper/2412.09991/) (2024)
- [COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking](https://mlsanity.com/paper/2504.01321/) (2025)
- [CLDTracker: A Comprehensive Language Description for Visual Tracking](https://mlsanity.com/paper/2505.23704/) (2025)
- [ATSTrack: Enhancing Visual-Language Tracking by Aligning Temporal and Spatial Scales](https://mlsanity.com/paper/2507.00454/) (2025)
- [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025)
- [UniSOT: A Unified Framework for Multi-Modality Single Object Tracking](https://mlsanity.com/paper/2511.01427/) (2025)
- [UETrack: A Unified and Efficient Framework for Single Object Tracking](https://mlsanity.com/paper/2603.01412/) (2026)
- [RAGTrack: Language-aware RGBT Tracking with Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.03617/) (2026)
- [4DVLT: Dynamic Scene Understanding with Worldline-Centered Vision-Language Tracking](https://mlsanity.com/paper/2606.22631/) (2026)
- [How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2512.07385/) (2025)
- [GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing](https://mlsanity.com/paper/2602.08550/) (2026)
- [Learning to Track Instance from Single Nature Language Description](https://mlsanity.com/paper/2605.07064/) (2026)
- [SENTRY: SAM2-Enhanced Neighbor-Aware and Temporally Reasoned Memory for Visual Tracking](https://mlsanity.com/paper/2606.24449/) (2026)
- [Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking](https://mlsanity.com/paper/2607.26511/) (2026)
- [GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2602.14771/) (2026)
- [Interactive Tracking: A Human-in-the-Loop Paradigm with Memory-Augmented Adaptation](https://mlsanity.com/paper/2604.01974/) (2026)
- [Dynamic Parsing and Updating Natural Language Specification using VLMs for Robust Vision-Language Tracking](https://mlsanity.com/paper/2606.29357/) (2026)
- [Models as Tools: An Agentic Coordination Framework for Unified Multimodal Visual Tracking](https://mlsanity.com/paper/2608.00847/) (2026)

## Abstract (arXiv)

Single object tracking aims to locate the target object in a video sequence according to the state specified by different modal references, including the initial bounding box (BBOX), natural language (NL), or both (NL+BBOX). Due to the gap between different modalities, most existing trackers are designed for single or partial of these reference settings and overspecialize on the specific modality. Differently, we present a unified tracker called UVLTrack, which can simultaneously handle all three reference settings (BBOX, NL, NL+BBOX) with the same parameters. The proposed UVLTrack enjoys several merits. First, we design a modality-unified feature extractor for joint visual and language feature learning and propose a multi-modal contrastive loss to align the visual and language features into a unified semantic space. Second, a modality-adaptive box head is proposed, which makes full use of the target reference to mine ever-changing scenario features dynamically from video contexts and distinguish the target in a contrastive way, enabling robust performance in different reference settings. Extensive experimental results demonstrate that UVLTrack achieves promising performance on seven visual tracking datasets, three vision-language tracking datasets, and three visual grounding datasets. Codes and models will be open-sourced at https://github.com/OpenSpaceAI/UVLTrack.