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Models/RDE

RDE

Reported on 34 benchmarks across 5 tasks · 2 papers · 17 SOTA

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

Computer Vision34 results

  • Text based Person RetrievalonICFG-PEDES
    R@10· uses extra data· 2023-08-19
    87.36
    best: 87.53 (Filtering-WoRA(Small))
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonICFG-PEDES
    R@5· uses extra data· 2023-08-19
    82.47
    best: 83.1 (Filtering-WoRA(Small))
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonRSTPReid
    mINP· 2023-08-19
    28.08
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonICFG-PEDES
    Rank 1· uses extra data· 2023-08-19
    66.54
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonICFG-PEDES
    Rank-10· uses extra data· 2023-08-19
    86.7
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonICFG-PEDES
    Rank-5· uses extra data· 2023-08-19
    81.7
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonICFG-PEDES
    mAP· uses extra data· 2023-08-19
    39.08
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonRSTPReid
    Rank 1· uses extra data· 2023-08-19
    64.45
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonRSTPReid
    Rank 10· uses extra data· 2023-08-19
    90
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonRSTPReid
    Rank 5· uses extra data· 2023-08-19
    83.5
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonRSTPReid
    mAP· uses extra data· 2023-08-19
    49.78
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonRSTPReid
    mINP· uses extra data· 2023-08-19
    27.43
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonCUHK-PEDES
    Rank 10· uses extra data· 2023-08-19
    93.63
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonCUHK-PEDES
    Rank-1· uses extra data· 2023-08-19
    74.46
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonCUHK-PEDES
    Rank-5· uses extra data· 2023-08-19
    89.42
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonCUHK-PEDES
    mAP· uses extra data· 2023-08-19
    66.13
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonCUHK-PEDES
    mINP· uses extra data· 2023-08-19
    49.66
    SOTA
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonICFG-PEDES
    R@1· uses extra data· 2023-08-19
    67.68
    best: 68.51 (APTM)
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonICFG-PEDES
    mAP· uses extra data· 2023-08-19
    40.06
    best: 44.93 (MARS)
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonICFG-PEDES
    mINP· uses extra data· 2023-08-19
    7.87
    best: 7.93 (IRRA)
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonRSTPReid
    R@1· 2023-08-19
    65.35
    best: 67.55 (MARS)
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonRSTPReid
    R@10· 2023-08-19
    89.9
    best: 91.45 (APTM)
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonRSTPReid
    R@5· 2023-08-19
    83.95
    best: 88.2 (IRRA)
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text based Person RetrievalonRSTPReid
    mAP· 2023-08-19
    50.88
    best: 52.92 (MARS)
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • Text-based Person Retrieval with Noisy CorrespondenceonICFG-PEDES
    mINP· uses extra data· 2023-08-19
    7.55
    best: 8.77 (IVT)
    Noisy-Correspondence Learning for Text-to-Image Person Re-identificationarXiv:2308.09911
  • VideoonMOSE
    F· 2022-05-08
    52.9
    best: 75.8 (Cutie+ (base, MEGA))
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761
  • VideoonMOSE
    J· 2022-05-08
    44.6
    best: 67.6 (Cutie+ (base, MEGA))
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761
  • VideoonMOSE
    J&F· 2022-05-08
    48.8
    best: 77.9 (SAM2)
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761
  • Video Object SegmentationonMOSE
    F· 2022-05-08
    52.9
    best: 75.8 (Cutie+ (base, MEGA))
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761
  • Video Object SegmentationonMOSE
    J· 2022-05-08
    44.6
    best: 67.6 (Cutie+ (base, MEGA))
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761
  • Video Object SegmentationonMOSE
    J&F· 2022-05-08
    48.8
    best: 77.9 (SAM2)
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761
  • Semi-Supervised Video Object SegmentationonMOSE
    F· 2022-05-08
    52.9
    best: 75.8 (Cutie+ (base, MEGA))
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761
  • Semi-Supervised Video Object SegmentationonMOSE
    J· 2022-05-08
    44.6
    best: 67.6 (Cutie+ (base, MEGA))
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761
  • Semi-Supervised Video Object SegmentationonMOSE
    J&F· 2022-05-08
    48.8
    best: 77.9 (SAM2)
    Recurrent Dynamic Embedding for Video Object SegmentationarXiv:2205.03761