Efficient Language-to-Vision Feature Injection for Referring Single-Object Tracking

• URL: http://arxiv.org/abs/2608.29126v1
• Date: Sat, 29 Aug 2026 08:09:08 GMT
• Title: Efficient Language-to-Vision Feature Injection for Referring Single-Object Tracking
• Abstract summary: LVTrack is a pure transformer framework that introduces a mode-conditioned Gated Feature to adaptively regulate textual guidance.<n>It directly harnesses a frozen vision-language pretrained model, greatly reducing training cost and preserving strong language understanding.<n>To further improve temporal localization, LVTrack integrates hybrid relative-absolute positional encodings with a lightweight memory mechanism.
• Score: 21.780462979089886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Referring single-object tracking enables language-grounded target initialization and subsequent tracking by jointly leveraging semantic cues and visual templates. The core difficulty is to use language differently across stages: it is indispensable for grounding but can induce semantic drift during tracking when overemphasized. Meanwhile, current methods often require costly vision-language alignment training. We present LVTrack, a pure transformer framework that introduces a mode-conditioned Gated Feature Injector to adaptively regulate textual guidance and alleviate semantic drift. Together with targeted adaptations, it directly harnesses a frozen vision-language pretrained model, greatly reducing training cost and preserving strong language understanding. To further improve temporal localization, LVTrack integrates hybrid relative-absolute positional encodings with a lightweight memory mechanism and optimizes autoregressive box prediction using a Gaussian-smoothed KL loss. Extensive experiments on standard benchmarks demonstrate that LVTrack achieves strong performance.

Related papers

• Dynamic Parsing and Updating Natural Language Specification using VLMs for Robust Vision-Language Tracking [61.91472604294714]
  We propose a novel language dependency parsing mechanism to distill tracking principal components.<n>We exploit the powerful cross-modal understanding capability of the pre-trained vision-language model Qwen-VL.<n>Our method achieves consistent and superior tracking performance on multiple large-scale vision-language tracking benchmarks.
  arXiv  Detail & Related papers  (2026-06-28T12:12:18Z)
• USS: Unified Spatial-Semantic Prompts for Embodied Visual Tracking with Latent Dynamics Learning [18.003030497082623]
  Embodied Visual Tracking (EVT) requires an agent to continuously follow a specified target while actively moving through dynamic environments.<n>We propose a paradigm shift, reframing target indication in EVT from text-only specification to unified spatial-semantic prompting.<n>We introduce Unified Spatial-Semantic Prompts for Embodied Visual Tracking with Latent Dynamics Learning.
  arXiv  Detail & Related papers  (2026-06-24T14:25:05Z)
• SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control [72.5651722107621]
  SCRIPT is a scalable diffusion policy with a multi-stage training framework for language-driven physics-based humanoid control.<n>The core of SCRIPT is a Joint Action-State-Text Diffusion Transformer (JAST-DiT), which represents actions, physical states, and text as dedicated token streams.<n>To stabilize autore control, we introduce a nonlinear history conditioning mechanism, which preserves the dense recent context and samples increasingly sparse cues from long-term history.
  arXiv  Detail & Related papers  (2026-05-21T14:17:21Z)
• RELO: Reinforcement Learning to Localize for Visual Object Tracking [70.27265738642956]
  We introduce RELO, a REinforcement-learning-to-LOcalize method for visual object tracking.<n>RELO replaces handcrafted spatial priors with a localization policy learned over spatial positions.<n>We show that RELO achieves 57.5% AUC on LaSOText without template updates.
  arXiv  Detail & Related papers  (2026-05-08T07:34:29Z)
• VPTracker: Global Vision-Language Tracking via Visual Prompt and MLLM [45.56517073754981]
  Vision-Language Tracking aims to continuously localize objects described by a visual template and a language description.<n>Existing methods, however, are typically limited to local search, making them prone to failures under viewpoint changes.<n>We introduce the first global tracking framework based on Multimodal Large Language Models (VPTracker), exploiting their powerful semantic reasoning to locate targets.
  arXiv  Detail & Related papers  (2025-12-28T06:12:28Z)
• ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking [0.6143225301480709]
  Vision-language tracking aims to locate the target object in the video sequence using a template patch and a language description provided in the initial frame.<n>To achieve robust tracking, it is essential not only to characterize the target features but also to utilize the context features related to the target.<n>We present a novel tracker named ATCTrack, which can obtain multimodal cues Aligned with the dynamic target states.
  arXiv  Detail & Related papers  (2025-07-26T09:05:12Z)
• TLControl: Trajectory and Language Control for Human Motion Synthesis [68.09806223962323]
  We present TLControl, a novel method for realistic human motion synthesis. It incorporates both low-level Trajectory and high-level Language semantics controls. It is practical for interactive and high-quality animation generation.
  arXiv  Detail & Related papers  (2023-11-28T18:54:16Z)
• Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking [3.416427651955299]
  Single object tracking aims to locate one specific target in video sequences, given its initial state. Vision-Language (VL) tracking has emerged as a promising approach. We present a novel tracker that progressively explores target-centric semantics for VL tracking.
  arXiv  Detail & Related papers  (2023-11-28T02:28:12Z)
• Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction [61.16125290912494]
  $textEVL_textGen$ is a framework designed for the pre-training of visually conditioned language generation models. We show that our approach accelerates the training of vision-language models by a factor of 5 without a noticeable impact on overall performance.
  arXiv  Detail & Related papers  (2023-10-05T03:40:06Z)
• Towards Unified Token Learning for Vision-Language Tracking [65.96561538356315]
  We present a vision-language (VL) tracking pipeline, termed textbfMMTrack, which casts VL tracking as a token generation task. Our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target.
  arXiv  Detail & Related papers  (2023-08-27T13:17:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.