# How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language in Vision-Language Tracking

arXiv: https://arxiv.org/abs/2411.15600 · HTML: https://mlsanity.com/paper/2411.15600/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Builds on (most cited first, 26 of 26)

- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [LaSOT: A High-quality Large-scale Single Object Tracking Benchmark](https://mlsanity.com/paper/2009.03465/) (2020)
- [ODTrack: Online Dense Temporal Token Learning for Visual Tracking](https://mlsanity.com/paper/2401.01686/) (2024)
- [Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark](https://mlsanity.com/paper/2103.16746/) (2021)
- [Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers](https://mlsanity.com/paper/2403.10574/) (2024)
- [HIPTrack: Visual Tracking with Historical Prompts](https://mlsanity.com/paper/2311.02072/) (2023)
- [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)
- [Explicit Visual Prompts for Visual Object Tracking](https://mlsanity.com/paper/2401.03142/) (2024)
- [Elysium: Exploring Object-level Perception in Videos via MLLM](https://mlsanity.com/paper/2403.16558/) (2024)
- [Divert More Attention to Vision-Language Tracking](https://mlsanity.com/paper/2207.01076/) (2022)
- [Grounding-Tracking-Integration](https://mlsanity.com/paper/1912.06316/) (2019)
- [Unifying Visual and Vision-Language Tracking via Contrastive Learning](https://mlsanity.com/paper/2401.11228/) (2024)
- [Real-time Visual Object Tracking with Natural Language Description](https://mlsanity.com/paper/1907.11751/) (2019)
- [WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking](https://mlsanity.com/paper/2201.07425/) (2022)
- [DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM](https://mlsanity.com/paper/2405.12139/) (2024)
- [Context-Aware Integration of Language and Visual References for Natural Language Tracking](https://mlsanity.com/paper/2403.19975/) (2024)
- [Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking](https://mlsanity.com/paper/1811.10014/) (2018)
- [Global Instance Tracking: Locating Target More Like Humans](https://mlsanity.com/paper/2202.13073/) (2022)
- [WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark](https://mlsanity.com/paper/2405.19818/) (2024)
- [Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers](https://mlsanity.com/paper/1912.02048/) (2019)
- [RTracker: Recoverable Tracking via PN Tree Structured Memory](https://mlsanity.com/paper/2403.19242/) (2024)
- [DTVLT: A Multi-modal Diverse Text Benchmark for Visual Language Tracking Based on LLM](https://mlsanity.com/paper/2410.02492/) (2024)
- [Divert More Attention to Vision-Language Object Tracking](https://mlsanity.com/paper/2307.10046/) (2023)
- [SOTVerse: A User-defined Task Space of Single Object Tracking](https://mlsanity.com/paper/2204.07414/) (2022)
- [Visual Language Tracking with Multi-modal Interaction: A Robust Benchmark](https://mlsanity.com/paper/2409.08887/) (2024)

## Built on by (most cited first, 10 of 10)

- [NarrLV: Towards a Comprehensive Narrative-Centric Evaluation for Long Video Generation](https://mlsanity.com/paper/2507.11245/) (2025)
- [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)
- [CausalStep: A Benchmark for Explicit Stepwise Causal Reasoning in Videos](https://mlsanity.com/paper/2507.16878/) (2025)
- [Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning](https://mlsanity.com/paper/2510.01681/) (2025)
- [Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues](https://mlsanity.com/paper/2412.19648/) (2024)
- [Select Less, Reason More: Prioritizing Evidence Purity for Video Reasoning](https://mlsanity.com/paper/2510.15440/) (2025)
- [DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking](https://mlsanity.com/paper/2505.00752/) (2025)
- [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025)
- [Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating](https://mlsanity.com/paper/2606.09167/) (2026)

## Abstract (arXiv)

Vision-language tracking (VLT) extends traditional single object tracking by incorporating textual information, providing semantic guidance to enhance tracking performance under challenging conditions like fast motion and deformations. However, current VLT trackers often underperform compared to single-modality methods on multiple benchmarks, with semantic information sometimes becoming a "distraction." To address this, we propose VLTVerse, the first fine-grained evaluation framework for VLT trackers that comprehensively considers multiple challenge factors and diverse semantic information, hoping to reveal the role of language in VLT. Our contributions include: (1) VLTVerse introduces 10 sequence-level challenge labels and 6 types of multi-granularity semantic information, creating a flexible and multi-dimensional evaluation space for VLT; (2) leveraging 60 subspaces formed by combinations of challenge factors and semantic types, we conduct systematic fine-grained evaluations of three mainstream SOTA VLT trackers, uncovering their performance bottlenecks across complex scenarios and offering a novel perspective on VLT evaluation; (3) through decoupled analysis of experimental results, we examine the impact of various semantic types on specific challenge factors in relation to different algorithms, providing essential guidance for enhancing VLT across data, evaluation, and algorithmic dimensions. The VLTVerse, toolkit, and results will be available at urlhttp://metaverse.aitestunion.com.