Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/ContrasTR

ContrasTR

Reported on 21 benchmarks across 4 tasks · 1 paper · 3 SOTA

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

Computer Vision21 results

VideoonBDD100K test
mMOTA· 2023-11-14
42.8
SOTA
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Object TrackingonBDD100K test
mMOTA· 2023-11-14
42.8
SOTA
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Multiple Object TrackingonBDD100K test
mMOTA· 2023-11-14
42.8
SOTA
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
VideoonBDD100K test
mHOTA· 2023-11-14
46.1
best: 48.2 (SUSHI)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
VideoonBDD100K test
mIDF1· 2023-11-14
56.5
best: 60 (SUSHI)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
VideoonBDD100K val
mIDF1· 2023-11-14
52.9
best: 56.7 (UNINEXT-H)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
VideoonBDD100K val
mMOTA· 2023-11-14
41.7
best: 45.5 (ByteTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Multi-Object TrackingonMOT17
HOTA· 2023-11-14
58.9
best: 67.1 (TrackTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Multi-Object TrackingonMOT17
IDF1· 2023-11-14
71.8
best: 83.1 (TrackTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Multi-Object TrackingonMOT17
MOTA· 2023-11-14
73.7
best: 81.8 (TrackTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Object TrackingonMOT17
HOTA· 2023-11-14
58.9
best: 67.1 (TrackTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Object TrackingonMOT17
IDF1· 2023-11-14
71.8
best: 83.1 (TrackTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Object TrackingonMOT17
MOTA· 2023-11-14
73.7
best: 81.8 (TrackTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Object TrackingonBDD100K test
mHOTA· 2023-11-14
46.1
best: 48.2 (SUSHI)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Object TrackingonBDD100K test
mIDF1· 2023-11-14
56.5
best: 60 (SUSHI)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Object TrackingonBDD100K val
mIDF1· 2023-11-14
52.9
best: 56.7 (UNINEXT-H)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Object TrackingonBDD100K val
mMOTA· 2023-11-14
41.7
best: 45.5 (ByteTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Multiple Object TrackingonBDD100K test
mHOTA· 2023-11-14
46.1
best: 48.2 (SUSHI)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Multiple Object TrackingonBDD100K test
mIDF1· 2023-11-14
56.5
best: 60 (SUSHI)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Multiple Object TrackingonBDD100K val
mIDF1· 2023-11-14
52.9
best: 56.7 (UNINEXT-H)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043
Multiple Object TrackingonBDD100K val
mMOTA· 2023-11-14
41.7
best: 45.5 (ByteTrack)
Contrastive Learning for Multi-Object Tracking with Transformers arXiv:2311.08043