Visual Object Tracking On Needforspeed
Métriques
AUC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AUC | Paper Title | Repository |
|---|---|---|---|
| SeqTrack-L384 | 0.662 | Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking | |
| DiMP-NCE+ | 0.65 | How to Train Your Energy-Based Model for Regression | |
| SAMURAI-L | 0.692 | SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory | - |
| ARTrackV2-L | 0.684 | ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe | |
| LoRAT-L-378 | 0.667 | Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance | |
| HIPTrack | 0.681 | HIPTrack: Visual Tracking with Historical Prompts | |
| PiVOT-L | 0.682 | Improving Visual Object Tracking through Visual Prompting | |
| LoRAT-g-378 | 0.681 | Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance | |
| AiATrack | 0.679 | AiATrack: Attention in Attention for Transformer Visual Tracking |
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