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Visual Object Tracking On Needforspeed

Metrics

AUC

Results

Performance results of various models on this benchmark

Paper Title
SAMURAI-L0.692SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
ARTrackV2-L0.684ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
PiVOT-L0.682Improving Visual Object Tracking through Visual Prompting
HIPTrack0.681HIPTrack: Visual Tracking with Historical Prompts
LoRAT-g-3780.681Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance
AiATrack0.679AiATrack: Attention in Attention for Transformer Visual Tracking
LoRAT-L-3780.667Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance
SeqTrack-L3840.662Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
DiMP-NCE+0.65How to Train Your Energy-Based Model for Regression
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Visual Object Tracking On Needforspeed | SOTA | HyperAI