← 研踪 PaperTrailCVF · CVPR 2026:cvf:8a86f879a74d4173e766df065eeecab5 · 记录 #1
MVLM: Template-Free Tracking via Vision-Language Margin Confidence and Memory-Gated Tracking
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:51
摘要
We introduce a new template-free tracking paradigm based solely on natural language, capable of tracking an arbitrary object and seamlessly switching to a new target without box initialization.Our key idea is to localize an object via vision-language (VL) correlation.However, using the correlation alone is brittle under large search regions due to spatial uncertainty and ambiguous VL saliency. To resolve these, we propose MVLM, a memory-based vision-language margin confidence that integrates vision-language correlation, encoder prediction, and temporal memory.MVLM dynamically gates the search region--switching between compact ROI (Region of Interest) search and global re-localization--to reduce spatial uncertainty. Theoretically, we derive bounds that connect the MVLM score to tracking probability, characterizing mis-localization within ROI and ROI-exclusion probabilities.Through extensive evaluation, we validate our theorems and achieve state-of-the-art performance on several benchmarks (TNL2K, LaSOT, OTB99 and MGIT) using only language guidance.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
MVLM: Template-Free Tracking via Vision-Language Margin Confidence and Memory-Gated Tracking
We introduce a new template-free tracking paradigm based solely on natural language, capable of tracking an arbitrary object and seamlessly switching to a new target without box initialization.Our key idea is to localize an object via vision-language (VL) correlation.However, using the correlation alone is brittle under large search regions due to spatial uncertainty and ambiguous VL saliency. To resolve these, we propose MVLM, a memory-based vision-language margin confidence that integrates vision-language correlation, encoder prediction, and temporal memory.MVLM dynamically gates the search region--switching between compact ROI (Region of Interest) search and global re-localization--to reduce spatial uncertainty. Theoretically, we derive bounds that connect the MVLM score to tracking probability, characterizing mis-localization within ROI and ROI-exclusion probabilities.Through extensive evaluation, we validate our theorems and achieve state-of-the-art performance on several benchmarks (TNL2K, LaSOT, OTB99 and MGIT) using only language guidance.
查看此版本解读 →查看此版本原文 ↗