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Models/SEA

SEA

Reported on 7 benchmarks across 2 tasks · 2 papers · 7 SOTA

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

Computer Vision4 results

  • Ad-hoc video searchonTRECVID-AVS17 (IACC.3)
    infAP· uses extra data· 2020-11-24
    0.234
    best: 0.29 (LAFF)
    SOTA
    SEA: Sentence Encoder Assembly for Video Retrieval by Textual QueriesarXiv:2011.12091
  • Ad-hoc video searchonTRECVID-AVS18 (IACC.3)
    infAP· uses extra data· 2020-11-24
    0.128
    best: 0.147 (LAFF)
    SOTA
    SEA: Sentence Encoder Assembly for Video Retrieval by Textual QueriesarXiv:2011.12091
  • Ad-hoc video searchonTRECVID-AVS16 (IACC.3)
    infAP· uses extra data· 2020-11-24
    0.164
    best: 0.222 (LAFF)
    SOTA
    SEA: Sentence Encoder Assembly for Video Retrieval by Textual QueriesarXiv:2011.12091
  • Ad-hoc video searchonTRECVID-AVS19 (V3C1)
    infAP· uses extra data· 2020-11-24
    0.167
    best: 0.192 (LAFF)
    SOTA
    SEA: Sentence Encoder Assembly for Video Retrieval by Textual QueriesarXiv:2011.12091

Miscellaneous3 results

  • Malware ClassificationonMicrosoft Malware Classification Challenge
    Accuracy (10-fold)· 2023-02-11
    0.9912
    best: 9641 (MalConv)
    SOTA
    Sequential Embedding-based Attentive (SEA) classifier for malware classificationarXiv:2302.05728
  • Malware ClassificationonMicrosoft Malware Classification Challenge
    LogLoss· 2023-02-11
    0.0431
    best: 0.0244 (Opcode-based Shallow CNN)
    SOTA
    Sequential Embedding-based Attentive (SEA) classifier for malware classificationarXiv:2302.05728
  • Malware ClassificationonMicrosoft Malware Classification Challenge
    Macro F1 (10-fold)· 2023-02-11
    0.9908
    best: 0.9951 (HYDRA)
    SOTA
    Sequential Embedding-based Attentive (SEA) classifier for malware classificationarXiv:2302.05728