The 3rd Anti-UAV Workshop & Challenge: Methods and Results
원문 초록
The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two main differences between this year's competition and the previous two. First, we have expanded the existing dataset, and for the first time, released a training set so that participants can focus on improving their models. Second, we set up two tracks for the first time, i.e., Anti-UAV Tracking and Anti-UAV Detection & Tracking. Around 76 participating teams from the globe competed in the 3rd Anti-UAV Challenge. In this paper, we provide a brief summary of the 3rd Anti-UAV Workshop & Challenge including brief introductions to the top three methods in each track. The submission leaderboard will be reopened for researchers that are interested in the Anti-UAV challenge. The benchmark dataset and other information can be found at: https://anti-uav.github.io/.
함께 살펴볼 논문
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HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking
UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions
Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking
Visual and Memory Dual Adapter for Multi-Modal Object Tracking
R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
토론
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