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Visual Object Tracking On Tnl2K

Métriques

AUC

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AUC	Paper Title	Repository
MCITrack-B224	62.9	Exploring Enhanced Contextual Information for Video-Level Object Tracking	-
RTracker-L	60.6	RTracker: Recoverable Tracking via PN Tree Structured Memory	-
MixFormerV2-B	57.4	-	-
LoRAT-g-378	62.7	Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance	-
MCITrack-L384	65.3	Exploring Enhanced Contextual Information for Video-Level Object Tracking	-
ARTrackV2-L	61.6	ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe	-
SeqTrack-L384	57.8	Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking	-
AdaSwitcher	-	Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark	-
ODTrack-B	60.9	ODTrack: Online Dense Temporal Token Learning for Visual Tracking	-
ARTrack-L	60.3	Autoregressive Visual Tracking
ODTrack-L	61.7	ODTrack: Online Dense Temporal Token Learning for Visual Tracking	-
DropTrack	56.9	DropMAE: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching Tasks	-
LoRAT-L-378	62.3	Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance	-

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Visual Object Tracking On Tnl2K | SOTA | HyperAI