GLAD: Generative Language-Assisted Visual Tracking for Low-Semantic Templates

2026-01-31

AI Summary

This work addresses the degradation in cross-modal alignment caused by low-semantic template images—such as blurry or low-resolution frames—in vision-language tracking, which adversely impacts target localization accuracy. To mitigate this issue, the paper introduces diffusion models into the task for the first time, proposing a generative language-assisted visual tracking approach. By fusing textual descriptions with template images through a diffusion process in latent space, the method enhances semantic compatibility between modalities, effectively alleviating modality misalignment under low-semantic conditions. Built upon a Transformer architecture, the framework enables efficient multimodal generative modeling. It achieves state-of-the-art performance across multiple benchmarks, significantly improving tracking accuracy with degraded or semantically weak templates while maintaining high inference efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Vision-language tracking has gained increasing attention in many scenarios. This task simultaneously deals with visual and linguistic information to localize objects in videos. Despite its growing utility, the development of vision-language tracking methods remains in its early stage. Current vision-language trackers usually employ Transformer architectures for interactive integration of template, search, and text features. However, persistent challenges about low-semantic images including prevalent image blurriness, low resolution and so on, may compromise model performance through degraded cross-modal understanding. To solve this problem, language assistance is usually used to deal with the obstacles posed by low-semantic images. However, due to the existing gap between current textual and visual features, direct concatenation and fusion of these features may have limited effectiveness. To address these challenges, we introduce a pioneering Generative Language-AssisteD tracking model, GLAD, which utilizes diffusion models for the generative multi-modal fusion of text description and template image to bolster compatibility between language and image and enhance template image semantic information. Our approach demonstrates notable improvements over the existing fusion paradigms. Blurry and semantically ambiguous template images can be restored to improve multi-modal features in the generative fusion paradigm. Experiments show that our method establishes a new state-of-the-art on multiple benchmarks and achieves an impressive inference speed. The code and models will be released at: https://github.com/Confetti-lxy/GLAD

Problem

• vision-language tracking
• low-semantic templates
• cross-modal understanding
• feature fusion
• template image degradation

Innovation

• diffusion models
• generative fusion
• vision-language tracking
• low-semantic templates
• multimodal compatibility