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Models/CINM

CINM

Reported on 63 benchmarks across 3 tasks · 1 paper · 24 SOTA

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

Computer Vision63 results

  • VideoonDAVIS 2017 (val)
    F-measure (Mean)· 2018-03-26
    74
    best: 93.4 (Cutie+ (base))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (val)
    F-measure (Recall)· 2018-03-26
    81.6
    best: 94.6 (STCN)
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (val)
    J&F· 2018-03-26
    70.6
    best: 90.7 (SAM2)
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (val)
    Jaccard (Mean)· 2018-03-26
    67.2
    best: 87.5 (Cutie+ (base))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (val)
    Jaccard (Recall)· 2018-03-26
    74.5
    best: 91.4 (ISVOS (MS))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Mean)· 2018-03-26
    70.5
    best: 91.4 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (test-dev)
    J&F· 2018-03-26
    67.5
    best: 88.1 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Mean)· 2018-03-26
    64.5
    best: 84.7 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· 2018-03-26
    74
    best: 93.4 (Cutie+ (base))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Recall)· 2018-03-26
    81.6
    best: 94.6 (STCN)
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (val)
    J&F· 2018-03-26
    70.6
    best: 90.7 (SAM2)
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· 2018-03-26
    67.2
    best: 87.5 (Cutie+ (base))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Recall)· 2018-03-26
    74.5
    best: 91.4 (ISVOS (MS))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Mean)· 2018-03-26
    70.5
    best: 91.4 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    J&F· 2018-03-26
    67.5
    best: 88.1 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Mean)· 2018-03-26
    64.5
    best: 84.7 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· 2018-03-26
    74
    best: 93.4 (Cutie+ (base))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Recall)· 2018-03-26
    81.6
    best: 94.6 (STCN)
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    J&F· 2018-03-26
    70.6
    best: 90.7 (SAM2)
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· 2018-03-26
    67.2
    best: 87.5 (Cutie+ (base))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Recall)· 2018-03-26
    74.5
    best: 91.4 (ISVOS (MS))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Mean)· 2018-03-26
    70.5
    best: 91.4 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    J&F· 2018-03-26
    67.5
    best: 88.1 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Mean)· 2018-03-26
    64.5
    best: 84.7 (Cutie+ (base, MEGA))
    SOTA
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (val)
    F-measure (Decay)· 2018-03-26
    26.2
    best: 85.3 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (val)
    Jaccard (Decay)· 2018-03-26
    24.6
    best: 32.5 (MuG-W)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2016
    F-measure (Decay)· 2018-03-26
    14.7
    best: 27.2 (OFL)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2016
    F-measure (Mean)· 2018-03-26
    85
    best: 94.7 (SwinB-DeAOT-L)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2016
    F-measure (Recall)· 2018-03-26
    92.1
    best: 97.1 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2016
    J&F· 2018-03-26
    84.2
    best: 93.4 (ISVOS (BL30K, MS))
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2016
    Jaccard (Decay)· 2018-03-26
    12.3
    best: 28.9 (BVS)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2016
    Jaccard (Mean)· 2018-03-26
    83.4
    best: 92.5 (ISVOS (BL30K, MS))
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2016
    Jaccard (Recall)· 2018-03-26
    94.9
    best: 98.1 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Decay)· 2018-03-26
    20
    best: 37.2 (RGMP)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Recall)· 2018-03-26
    79.6
    best: 89.7 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Decay)· 2018-03-26
    20
    best: 35.7 (RVOS)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Recall)· 2018-03-26
    73.8
    best: 85.5 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Decay)· 2018-03-26
    26.2
    best: 85.3 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Decay)· 2018-03-26
    24.6
    best: 32.5 (MuG-W)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2016
    F-measure (Decay)· 2018-03-26
    14.7
    best: 27.2 (OFL)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2016
    F-measure (Mean)· 2018-03-26
    85
    best: 94.7 (SwinB-DeAOT-L)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2016
    F-measure (Recall)· 2018-03-26
    92.1
    best: 97.1 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2016
    J&F· 2018-03-26
    84.2
    best: 93.4 (ISVOS (BL30K, MS))
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2016
    Jaccard (Decay)· 2018-03-26
    12.3
    best: 28.9 (BVS)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2016
    Jaccard (Mean)· 2018-03-26
    83.4
    best: 92.5 (ISVOS (BL30K, MS))
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2016
    Jaccard (Recall)· 2018-03-26
    94.9
    best: 98.1 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Decay)· 2018-03-26
    20
    best: 37.2 (RGMP)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Recall)· 2018-03-26
    79.6
    best: 89.7 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Decay)· 2018-03-26
    20
    best: 35.7 (RVOS)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Recall)· 2018-03-26
    73.8
    best: 85.5 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Decay)· 2018-03-26
    26.2
    best: 85.3 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Decay)· 2018-03-26
    24.6
    best: 32.5 (MuG-W)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    F-measure (Decay)· 2018-03-26
    14.7
    best: 27.2 (OFL)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    F-measure (Mean)· 2018-03-26
    85
    best: 94.7 (SwinB-DeAOT-L)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    F-measure (Recall)· 2018-03-26
    92.1
    best: 97.1 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    J&F· 2018-03-26
    84.2
    best: 93.4 (ISVOS (BL30K, MS))
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    Jaccard (Decay)· 2018-03-26
    12.3
    best: 28.9 (BVS)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    Jaccard (Mean)· 2018-03-26
    83.4
    best: 92.5 (ISVOS (BL30K, MS))
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    Jaccard (Recall)· 2018-03-26
    94.9
    best: 98.1 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Decay)· 2018-03-26
    20
    best: 37.2 (RGMP)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Recall)· 2018-03-26
    79.6
    best: 89.7 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Decay)· 2018-03-26
    20
    best: 35.7 (RVOS)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Recall)· 2018-03-26
    73.8
    best: 85.5 (STCN)
    CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFarXiv:1803.09453