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Visual Object Tracking On Lasot Ext

Métriques

AUC

Precision

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AUC	Precision	Paper Title	Repository
DropTrack	52.7	60.2	DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks
LoRAT-g-378	56.5	64.9	Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance
ODTrack-L	53.9	-	ODTrack: Online Dense Temporal Token Learning for Visual Tracking
ODTrack-B	52.4	-	ODTrack: Online Dense Temporal Token Learning for Visual Tracking
HIPTrack	53.0	60.6	HIPTrack: Visual Tracking with Historical Prompts
DAM4SAM	60.9	-	A Distractor-Aware Memory for Visual Object Tracking with SAM2
ARTrack-L	52.8	59.7	Autoregressive Visual Tracking
MCITrack-L384	55.7	62.9	Exploring Enhanced Contextual Information for Video-Level Object Tracking
UNINEXT-H	56.2	63.8	Universal Instance Perception as Object Discovery and Retrieval
KeepTrack	48.2	-	Learning Target Candidate Association to Keep Track of What Not to Track
MCITrack-B224	54.6	62.1	Exploring Enhanced Contextual Information for Video-Level Object Tracking
ARTrackV2-L	53.4	60.2	ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
LoRAT-L-378	56.6	65.1	Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance
SAMURAI-L	61.0	72.2	SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory	-
OSTrack	50.6	57.6	Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
SeqTrack-L384	50.7	57.5	Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
LTMU	41.4	47.3	High-Performance Long-Term Tracking with Meta-Updater
RTracker-L	54.9	62.7	RTracker: Recoverable Tracking via PN Tree Structured Memory

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