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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: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching 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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Visual Object Tracking On Lasot Ext | SOTA | HyperAI