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Models/OCLR

OCLR

Reported on 6 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 Vision6 results

  • Instance SegmentationonSegTrack-v2
    mIoU· 2022-07-05
    67.6
    best: 79.6 (RCF (with post-processing))
    SOTA
    Segmenting Moving Objects via an Object-Centric Layered RepresentationarXiv:2207.02206
  • Instance SegmentationonFBMS-59
    mIoU· 2022-07-05
    65.4
    best: 72.4 (RCF (with post-processing))
    SOTA
    Segmenting Moving Objects via an Object-Centric Layered RepresentationarXiv:2207.02206
  • Instance SegmentationonDAVIS 2016
    J score· uses extra data· 2022-07-05
    72.1
    best: 83 (RCF (with Post-Processing))
    SOTA
    Segmenting Moving Objects via an Object-Centric Layered RepresentationarXiv:2207.02206
  • Unsupervised Object SegmentationonSegTrack-v2
    mIoU· 2022-07-05
    67.6
    best: 79.6 (RCF (with post-processing))
    SOTA
    Segmenting Moving Objects via an Object-Centric Layered RepresentationarXiv:2207.02206
  • Unsupervised Object SegmentationonFBMS-59
    mIoU· 2022-07-05
    65.4
    best: 72.4 (RCF (with post-processing))
    SOTA
    Segmenting Moving Objects via an Object-Centric Layered RepresentationarXiv:2207.02206
  • Unsupervised Object SegmentationonDAVIS 2016
    J score· uses extra data· 2022-07-05
    72.1
    best: 83 (RCF (with Post-Processing))
    SOTA
    Segmenting Moving Objects via an Object-Centric Layered RepresentationarXiv:2207.02206