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SOTA
Visual Object Tracking
Visual Object Tracking On Got 10K
Visual Object Tracking On Got 10K
Metrics
Average Overlap
Success Rate 0.5
Success Rate 0.75
Results
Performance results of various models on this benchmark
Columns
Model Name
Average Overlap
Success Rate 0.5
Success Rate 0.75
Paper Title
Repository
SLT-TransT
67.5
76.8
60.3
Towards Sequence-Level Training for Visual Tracking
-
OSTrack-384
73.7
83.2
70.8
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
-
MCITrack-B224
77.9
88.2
76.8
Exploring Enhanced Contextual Information for Video-Level Object Tracking
-
MCITrack-L384
80.0
88.5
80.2
Exploring Enhanced Contextual Information for Video-Level Object Tracking
-
DiMP
61.1
71.7
-
Learning Discriminative Model Prediction for Tracking
-
SeqTrack-L384
74.8
81.9
72.2
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
-
Siam R-CNN
64.9
72.8
-
Siam R-CNN: Visual Tracking by Re-Detection
-
MITS
80.4
89.8
75.8
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation
-
STMTrack
64.2
73.7
57.5
STMTrack: Template-free Visual Tracking with Space-time Memory Networks
-
ATOM
61.0
74.2
-
ATOM: Accurate Tracking by Overlap Maximization
-
ARTrackV2-L
79.5
87.8
79.6
ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
-
TRASFUST
61.7
72.9
-
Tracking-by-Trackers with a Distilled and Reinforced Model
-
NeighborTrack-OSTrack
75.7
85.72
73.3
NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets
-
ARTrack-L
78.5
87.4
77.8
Autoregressive Visual Tracking
FEAR-M
62.3
-
-
FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
-
SAMURAI-L
81.7
92.2
76.9
SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
-
TATrack-L-GOT
76.6
85.7
73.4
Target-Aware Tracking with Long-term Context Attention
-
Ocean
61.1
72.1
-
Ocean: Object-aware Anchor-free Tracking
-
MixFormer-1k
71.2
79.9
65.8
MixFormer: End-to-End Tracking with Iterative Mixed Attention
-
FEAR-L
64.5
-
-
FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
-
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