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Models/SSN

SSN

Reported on 76 benchmarks across 12 tasks · 3 papers · 34 SOTA

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

Methodology34 results

  • Zero-Shot LearningonHACS
    Average-mAP· 2017-12-26
    18.97
    best: 45.8 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Zero-Shot LearningonHACS
    mAP@0.5· 2017-12-26
    28.82
    best: 66.4 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Zero-Shot LearningonHACS
    mAP@0.75· 2017-12-26
    18.8
    best: 47.2 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Zero-Shot LearningonHACS
    mAP@0.95· 2017-12-26
    5.32
    best: 14.3 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Zero-Shot LearningonActivityNet-1.3
    mAP· 2017-03-08
    32.26
    best: 42.9 (RDFA-S6 (InternVideo2-6B))
    SOTA
    A Pursuit of Temporal Accuracy in General Activity DetectionarXiv:1703.02716
  • Zero-Shot LearningonActivityNet-1.3
    mAP IOU@0.5· 2017-03-08
    39.12
    best: 64.1 (RDFA-S6 (InternVideo2-6B))
    SOTA
    A Pursuit of Temporal Accuracy in General Activity DetectionarXiv:1703.02716
  • 3DonnuScenes
    NDS
    0.62
    best: 55.3 (LabelDistill)
  • 3DonnuScenes
    mAAE
    0.09
    best: 1 (BirdNet+ (multisweep))
  • 3DonnuScenes
    mAOE
    0.43
    best: 1.6 (PointNet)
  • 3DonnuScenes
    mAP
    0.51
    best: 45.1 (LabelDistill)
  • 3DonnuScenes
    mASE
    0.24
    best: 1 (qww)
  • 3DonnuScenes
    mATE
    0.34
    best: 1.06 (3D-GCK)
  • 3DonnuScenes
    mAVE
    0.27
    best: 2.21 (PointNet)
  • 2D ClassificationonnuScenes
    NDS
    0.62
    best: 55.3 (LabelDistill)
  • 2D ClassificationonnuScenes
    mAAE
    0.09
    best: 1 (BirdNet+ (multisweep))
  • 2D ClassificationonnuScenes
    mAOE
    0.43
    best: 1.6 (PointNet)
  • 2D ClassificationonnuScenes
    mAP
    0.51
    best: 45.1 (LabelDistill)
  • 2D ClassificationonnuScenes
    mASE
    0.24
    best: 1 (qww)
  • 2D ClassificationonnuScenes
    mATE
    0.34
    best: 1.06 (3D-GCK)
  • 2D ClassificationonnuScenes
    mAVE
    0.27
    best: 2.21 (PointNet)
  • 2D Object DetectiononnuScenes
    NDS
    0.62
    best: 55.3 (LabelDistill)
  • 2D Object DetectiononnuScenes
    mAAE
    0.09
    best: 1 (BirdNet+ (multisweep))
  • 2D Object DetectiononnuScenes
    mAOE
    0.43
    best: 1.6 (PointNet)
  • 2D Object DetectiononnuScenes
    mAP
    0.51
    best: 45.1 (LabelDistill)
  • 2D Object DetectiononnuScenes
    mASE
    0.24
    best: 1 (qww)
  • 2D Object DetectiononnuScenes
    mATE
    0.34
    best: 1.06 (3D-GCK)
  • 2D Object DetectiononnuScenes
    mAVE
    0.27
    best: 2.21 (PointNet)
  • 16konnuScenes
    NDS
    0.62
    best: 55.3 (LabelDistill)
  • 16konnuScenes
    mAAE
    0.09
    best: 1 (BirdNet+ (multisweep))
  • 16konnuScenes
    mAOE
    0.43
    best: 1.6 (PointNet)
  • 16konnuScenes
    mAP
    0.51
    best: 45.1 (LabelDistill)
  • 16konnuScenes
    mASE
    0.24
    best: 1 (qww)
  • 16konnuScenes
    mATE
    0.34
    best: 1.06 (3D-GCK)
  • 16konnuScenes
    mAVE
    0.27
    best: 2.21 (PointNet)

Computer Vision32 results

  • VideoonHACS
    Average-mAP· 2017-12-26
    18.97
    best: 45.8 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • VideoonHACS
    mAP@0.5· 2017-12-26
    28.82
    best: 66.4 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • VideoonHACS
    mAP@0.75· 2017-12-26
    18.8
    best: 47.2 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • VideoonHACS
    mAP@0.95· 2017-12-26
    5.32
    best: 14.3 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Temporal Action LocalizationonHACS
    Average-mAP· 2017-12-26
    18.97
    best: 45.8 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Temporal Action LocalizationonHACS
    mAP@0.5· 2017-12-26
    28.82
    best: 66.4 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Temporal Action LocalizationonHACS
    mAP@0.75· 2017-12-26
    18.8
    best: 47.2 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Temporal Action LocalizationonHACS
    mAP@0.95· 2017-12-26
    5.32
    best: 14.3 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Action LocalizationonHACS
    Average-mAP· 2017-12-26
    18.97
    best: 45.8 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Action LocalizationonHACS
    mAP@0.5· 2017-12-26
    28.82
    best: 66.4 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Action LocalizationonHACS
    mAP@0.75· 2017-12-26
    18.8
    best: 47.2 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • Action LocalizationonHACS
    mAP@0.95· 2017-12-26
    5.32
    best: 14.3 (RDFA-S6 (InternVideo2-6B))
    SOTA
    HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationarXiv:1712.09374
  • VideoonActivityNet-1.3
    mAP· 2017-03-08
    32.26
    best: 42.9 (RDFA-S6 (InternVideo2-6B))
    SOTA
    A Pursuit of Temporal Accuracy in General Activity DetectionarXiv:1703.02716
  • VideoonActivityNet-1.3
    mAP IOU@0.5· 2017-03-08
    39.12
    best: 64.1 (RDFA-S6 (InternVideo2-6B))
    SOTA
    A Pursuit of Temporal Accuracy in General Activity DetectionarXiv:1703.02716
  • Temporal Action LocalizationonActivityNet-1.3
    mAP· 2017-03-08
    32.26
    best: 42.9 (RDFA-S6 (InternVideo2-6B))
    SOTA
    A Pursuit of Temporal Accuracy in General Activity DetectionarXiv:1703.02716
  • Temporal Action LocalizationonActivityNet-1.3
    mAP IOU@0.5· 2017-03-08
    39.12
    best: 64.1 (RDFA-S6 (InternVideo2-6B))
    SOTA
    A Pursuit of Temporal Accuracy in General Activity DetectionarXiv:1703.02716
  • Action LocalizationonActivityNet-1.3
    mAP· 2017-03-08
    32.26
    best: 42.9 (RDFA-S6 (InternVideo2-6B))
    SOTA
    A Pursuit of Temporal Accuracy in General Activity DetectionarXiv:1703.02716
  • Action LocalizationonActivityNet-1.3
    mAP IOU@0.5· 2017-03-08
    39.12
    best: 64.1 (RDFA-S6 (InternVideo2-6B))
    SOTA
    A Pursuit of Temporal Accuracy in General Activity DetectionarXiv:1703.02716
  • Object DetectiononnuScenes
    NDS
    0.62
    best: 55.3 (LabelDistill)
  • Object DetectiononnuScenes
    mAAE
    0.09
    best: 1 (BirdNet+ (multisweep))
  • Object DetectiononnuScenes
    mAOE
    0.43
    best: 1.6 (PointNet)
  • Object DetectiononnuScenes
    mAP
    0.51
    best: 45.1 (LabelDistill)
  • Object DetectiononnuScenes
    mASE
    0.24
    best: 1 (qww)
  • Object DetectiononnuScenes
    mATE
    0.34
    best: 1.06 (3D-GCK)
  • Object DetectiononnuScenes
    mAVE
    0.27
    best: 2.21 (PointNet)
  • 3D Object DetectiononnuScenes
    NDS
    0.62
    best: 55.3 (LabelDistill)
  • 3D Object DetectiononnuScenes
    mAAE
    0.09
    best: 1 (BirdNet+ (multisweep))
  • 3D Object DetectiononnuScenes
    mAOE
    0.43
    best: 1.6 (PointNet)
  • 3D Object DetectiononnuScenes
    mAP
    0.51
    best: 45.1 (LabelDistill)
  • 3D Object DetectiononnuScenes
    mASE
    0.24
    best: 1 (qww)
  • 3D Object DetectiononnuScenes
    mATE
    0.34
    best: 1.06 (3D-GCK)
  • 3D Object DetectiononnuScenes
    mAVE
    0.27
    best: 2.21 (PointNet)

Robots5 results

  • Activity RecognitiononTHUMOS’14
    mAP@0.1· 2017-04-20
    66
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228
  • Activity RecognitiononTHUMOS’14
    mAP@0.2· 2017-04-20
    59.4
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228
  • Activity RecognitiononTHUMOS’14
    mAP@0.3· 2017-04-20
    51.9
    best: 56 (BMN)
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228
  • Activity RecognitiononTHUMOS’14
    mAP@0.4· 2017-04-20
    41
    best: 47.4 (BMN)
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228
  • Activity RecognitiononTHUMOS’14
    mAP@0.5· 2017-04-20
    29.8
    best: 38.8 (BMN)
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228

Time Series5 results

  • Action RecognitiononTHUMOS’14
    mAP@0.1· 2017-04-20
    66
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228
  • Action RecognitiononTHUMOS’14
    mAP@0.2· 2017-04-20
    59.4
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228
  • Action RecognitiononTHUMOS’14
    mAP@0.3· 2017-04-20
    51.9
    best: 56 (BMN)
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228
  • Action RecognitiononTHUMOS’14
    mAP@0.4· 2017-04-20
    41
    best: 47.4 (BMN)
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228
  • Action RecognitiononTHUMOS’14
    mAP@0.5· 2017-04-20
    29.8
    best: 38.8 (BMN)
    SOTA
    Temporal Action Detection with Structured Segment NetworksarXiv:1704.06228