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Models/MITS

MITS

Reported on 18 benchmarks across 2 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision18 results

Object TrackingonGOT-10k
Average Overlap· 2023-08-25
80.4
best: 81.7 (SAMURAI-L)
SOTA
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Object TrackingonGOT-10k
Success Rate 0.5· 2023-08-25
89.8
best: 92.2 (SAMURAI-L)
SOTA
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Object TrackingonGOT-10k
Success Rate 0.75· 2023-08-25
75.8
best: 82.3 (SPMTrack-G)
SOTA
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonGOT-10k
Average Overlap· 2023-08-25
80.4
best: 81.7 (SAMURAI-L)
SOTA
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonGOT-10k
Success Rate 0.5· 2023-08-25
89.8
best: 92.2 (SAMURAI-L)
SOTA
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonGOT-10k
Success Rate 0.75· 2023-08-25
75.8
best: 82.3 (SPMTrack-G)
SOTA
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Object TrackingonLaSOT
AUC· 2023-08-25
72
best: 77.4 (SPMTrack-G)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Object TrackingonLaSOT
Normalized Precision· 2023-08-25
80.1
best: 86.6 (SPMTrack-G)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Object TrackingonLaSOT
Precision· 2023-08-25
78.5
best: 85 (SPMTrack-G)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Object TrackingonTrackingNet
Accuracy· 2023-08-25
83.4
best: 87.9 (MCITrack-L384)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Object TrackingonTrackingNet
Normalized Precision· 2023-08-25
88.9
best: 92.1 (MCITrack-L384)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Object TrackingonTrackingNet
Precision· 2023-08-25
84.6
best: 89.2 (MCITrack-L384)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonLaSOT
AUC· 2023-08-25
72
best: 77.4 (SPMTrack-G)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonLaSOT
Normalized Precision· 2023-08-25
80.1
best: 86.6 (SPMTrack-G)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonLaSOT
Precision· 2023-08-25
78.5
best: 85 (SPMTrack-G)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonTrackingNet
Accuracy· 2023-08-25
83.4
best: 87.9 (MCITrack-L384)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonTrackingNet
Normalized Precision· 2023-08-25
88.9
best: 92.1 (MCITrack-L384)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266
Visual Object TrackingonTrackingNet
Precision· 2023-08-25
84.6
best: 89.2 (MCITrack-L384)
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation arXiv:2308.13266