Alapfogalmak
Proposing a novel RGB-T tracking method, M3PT, that leverages middle fusion and multi-stage, multi-form prompts to optimize performance and efficiency in RGB-T tracking.
Kivonat
The content discusses the challenges in RGB-T tracking, introduces the M3PT method, outlines the methodology, and presents evaluation results on the LasHer benchmark. It covers the introduction, related work, methodology, experiment details, and evaluation results comprehensively.
Introduction:
Visual object tracking importance in computer vision.
Challenges in RGB-T tracking due to extreme conditions.
Related Work:
Overview of RGB-T tracking methods.
Addressing data scarcity and fine-tuning challenges.
Methodology:
Introduction of M3PT method leveraging middle fusion and multi-stage prompts.
Detailed explanation of Uni-modal Exploration, Middle Fusion, Fusion-modal Enhancement, and Modality-aware and Stage-aware Prompt Strategies.
Experiment:
Details of the experimental setup and benchmarks used.
Evaluation results on LasHer benchmark, comparison with state-of-the-art methods.
Evaluation Results on LasHer:
Performance comparison with existing methods.
Evaluation curves for PR, NPR, and SR metrics.
Per-Attribute Evaluation:
Performance evaluation on 19 challenge attributes.
Statisztikák
Our method achieves PR, NPR, and SR scores of 67.3, 63.9, and 54.2 respectively.
Inference speed of our method reaches 46.1fps.
Idézetek
"Our method further unleashes the huge potential of prompt fine-tuning in RGB-T tracking tasks."