# Grounding-Tracking-Integration

arXiv: https://arxiv.org/abs/1912.06316 · HTML: https://mlsanity.com/paper/1912.06316/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 30 of 32)

- [Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark](https://mlsanity.com/paper/2103.16746/) (2021)
- [SAT: 2D Semantics Assisted Training for 3D Visual Grounding](https://mlsanity.com/paper/2105.11450/) (2021)
- [Towards Unified Token Learning for Vision-Language Tracking](https://mlsanity.com/paper/2308.14103/) (2023)
- [Joint Visual Grounding and Tracking with Natural Language Specification](https://mlsanity.com/paper/2303.12027/) (2023)
- [SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2403.16002/) (2024)
- [Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2211.02219/) (2022)
- [MeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation](https://mlsanity.com/paper/2512.10945/) (2025)
- [All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment](https://mlsanity.com/paper/2307.03373/) (2023)
- [Type-to-Track: Retrieve Any Object via Prompt-based Tracking](https://mlsanity.com/paper/2305.13495/) (2023)
- [DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM](https://mlsanity.com/paper/2405.12139/) (2024)
- [ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation](https://mlsanity.com/paper/2103.10702/) (2021)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [VideoMolmo: Spatio-Temporal Grounding Meets Pointing](https://mlsanity.com/paper/2506.05336/) (2025)
- [DTVLT: A Multi-modal Diverse Text Benchmark for Visual Language Tracking Based on LLM](https://mlsanity.com/paper/2410.02492/) (2024)
- [Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos](https://mlsanity.com/paper/2103.12346/) (2021)
- [Correspondence Matters for Video Referring Expression Comprehension](https://mlsanity.com/paper/2207.10400/) (2022)
- [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)
- [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)
- [Tracking and Understanding Object Transformations](https://mlsanity.com/paper/2511.04678/) (2025)
- [CLDTracker: A Comprehensive Language Description for Visual Tracking](https://mlsanity.com/paper/2505.23704/) (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)
- [DINTR: Tracking via Diffusion-based Interpolation](https://mlsanity.com/paper/2410.10053/) (2024)
- [ClickTrack: Towards Real-time Interactive Single Object Tracking](https://mlsanity.com/paper/2411.13183/) (2024)
- [GLAD: Generative Language-Assisted Visual Tracking for Low-Semantic Templates](https://mlsanity.com/paper/2602.00570/) (2026)
- [VLM-NCD:Novel Class Discovery with Vision-Based Large Language Models](https://mlsanity.com/paper/2512.10262/) (2025)

## Abstract (arXiv)

In this paper, we study Tracking by Language that localizes the target box sequence in a video based on a language query. We propose a framework called GTI that decomposes the problem into three sub-tasks: Grounding, Tracking, and Integration. The three sub-task modules operate simultaneously and predict the box sequence frame-by-frame. "Grounding" predicts the referred region directly from the language query. "Tracking" localizes the target based on the history of the grounded regions in previous frames. "Integration" generates final predictions by synergistically combining grounding and tracking. With the "integration" task as the key, we explore how to indicate the quality of the grounded regions in each frame and achieve the desired mutually beneficial combination. To this end, we propose an "RT-integration" method that defines and predicts two scores to guide the integration: 1) R-score represents the Region correctness whether the grounding prediction accurately covers the target, and 2) T-score represents the Template quality whether the region provides informative visual cues to improve tracking in future frames. We present our real-time GTI implementation with the proposed RT-integration, and benchmark the framework on LaSOT and Lingual OTB99 with highly promising results. Moreover, we produce a disambiguated version of LaSOT queries to facilitate future tracking by language studies.