6 papers
VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG
Honghao Fu, Miao Xu, Yiwei Wang +3
Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generation (RAG) is a promising remedy by organ…
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…
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…
DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM
Xuchen Li, Xiaokun Feng, Shiyu Hu +4
Visual Language Tracking (VLT) enhances single object tracking (SOT) by integrating natural language descriptions from a video, for the precise tracking of a specified object. By l…