Visual Object Tracking On Trackingnet

Métriques

Accuracy

Normalized Precision

Precision

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Normalized Precision	Precision	Paper Title	Repository
ARTrack-L	85.6	89.6	86.0	Autoregressive Visual Tracking
SiamFC++	74.5	79.8	68.5	SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines	-
ATOM(Resnet18)+EnergyRegression	-	80.1	69.7	Energy-Based Models for Deep Probabilistic Regression	-
NeighborTrack-OSTrack	83.79	88.30	-	NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets	-
GFS-DCF	60.9	71.79	56.57	Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking	-
SiamBAN-ACM	75.3	81.0	71.2	Learning to Fuse Asymmetric Feature Maps in Siamese Trackers	-
ODTrack-B	85.1	-	-	ODTrack: Online Dense Temporal Token Learning for Visual Tracking	-
DiMP-50	74.0	80.1	-	Learning Discriminative Model Prediction for Tracking	-
UNINEXT-H	85.4	89.0	86.4	Universal Instance Perception as Object Discovery and Retrieval	-
OSTrack-384	83.9	88.5	83.2	Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework	-
MixFormer-L	83.9	88.9	83.1	MixFormer: End-to-End Tracking with Iterative Mixed Attention	-
MixFormerV2-B	83.4	88.1	81.6	-	-
TREG	78.5	83.8	75	Target Transformed Regression for Accurate Tracking	-
SeqTrack-L384	85.5	89.8	85.8	Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking	-
ATOM	70.34	77.11	64.84	ATOM: Accurate Tracking by Overlap Maximization	-
AiATrack	82.7	87.8	80.4	AiATrack: Attention in Attention for Transformer Visual Tracking	-
LoRAT-g-378	86.0	90.2	86.1	Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance	-
SLT-TransT	82.8	87.5	81.4	Towards Sequence-Level Training for Visual Tracking	-
MCITrack-B224	86.3	90.9	86.1	Exploring Enhanced Contextual Information for Video-Level Object Tracking	-
TATrack-L	85.0	89.3	84.5	Target-Aware Tracking with Long-term Context Attention	-

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