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Models/LVNet

LVNet

Reported on 6 benchmarks across 2 tasks · 1 paper · 2 SOTA

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

Natural Language Processing3 results

  • Question AnsweringonIntentQA
    Accuracy· 2024-06-13
    71.1
    best: 71.5 (ENTER)
    SOTA
    Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QAarXiv:2406.09396
  • Question AnsweringonEgoSchema (fullset)
    Accuracy· 2024-06-13
    61.1
    best: 71.14 (BIMBA-LLaVA-Qwen2-7B)
    Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QAarXiv:2406.09396
  • Question AnsweringonEgoSchema (subset)
    Accuracy· 2024-06-13
    66
    best: 68.6 (Tarsier (34B))
    Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QAarXiv:2406.09396

Reasoning3 results

  • Video Question AnsweringonIntentQA
    Accuracy· 2024-06-13
    71.1
    best: 71.5 (ENTER)
    SOTA
    Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QAarXiv:2406.09396
  • Video Question AnsweringonEgoSchema (fullset)
    Accuracy· 2024-06-13
    61.1
    best: 71.14 (BIMBA-LLaVA-Qwen2-7B)
    Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QAarXiv:2406.09396
  • Video Question AnsweringonEgoSchema (subset)
    Accuracy· 2024-06-13
    66
    best: 68.6 (Tarsier (34B))
    Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QAarXiv:2406.09396