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Visual Object Tracking On Tnl2K

Metrics

AUC

Results

Performance results of various models on this benchmark

Model Name
AUC
Paper TitleRepository
MCITrack-B22462.9Exploring Enhanced Contextual Information for Video-Level Object Tracking-
RTracker-L60.6RTracker: Recoverable Tracking via PN Tree Structured Memory-
MixFormerV2-B57.4--
LoRAT-g-37862.7Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance-
MCITrack-L38465.3Exploring Enhanced Contextual Information for Video-Level Object Tracking-
ARTrackV2-L61.6ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe-
SeqTrack-L38457.8Unified 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-B60.9ODTrack: Online Dense Temporal Token Learning for Visual Tracking-
ARTrack-L60.3Autoregressive Visual Tracking
ODTrack-L61.7ODTrack: Online Dense Temporal Token Learning for Visual Tracking-
DropTrack56.9DropMAE: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching Tasks-
LoRAT-L-37862.3Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance-
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Visual Object Tracking On Tnl2K | SOTA | HyperAI