8 papers
ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking
X. Feng, S. Hu, X. Li +5
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. To achieve robust t…
IRGPT: Understanding Real-world Infrared Image with Bi-cross-modal Curriculum on Large-scale Benchmark
Zhe Cao, Jin Zhang, Ruiheng Zhang
Real-world infrared imagery presents unique challenges for vision-language models due to the scarcity of aligned text data and domain-specific characteristics. Although existing me…
FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning
Shiyu Hu, Xuchen Li, Xuzhao Li +4
Despite rapid progress in large vision-language models (LVLMs), existing video caption benchmarks remain limited in evaluating their alignment with human understanding. Most rely o…
Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues
X. Feng, D. Zhang, S. Hu +5
Vision-Language Tracking (VLT) aims to localize a target in video sequences using a visual template and language description. While textual cues enhance tracking potential, current…
How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language in Vision-Language Tracking
Xuchen Li, Shiyu Hu, Xiaokun Feng +4
Vision-language tracking (VLT) extends traditional single object tracking by incorporating textual information, providing semantic guidance to enhance tracking performance under ch…
DTVLT: A Multi-modal Diverse Text Benchmark for Visual Language Tracking Based on LLM
Xuchen Li, Shiyu Hu, Xiaokun Feng +4
Visual language tracking (VLT) has emerged as a cutting-edge research area, harnessing linguistic data to enhance algorithms with multi-modal inputs and broadening the scope of tra…