pith:7L2LCIDI
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
A scalable LLM-based method creates a 7 million video dataset that trains models with leading zero-shot action recognition.
arxiv:2307.06942 v2 · 2023-07-13 · cs.CV
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\pithnumber{7L2LCIDIL22PSJFQG4OCMYKWBU}
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Claims
Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance.
The multi-scale LLM-generated descriptions are sufficiently accurate and diverse to produce transferable video-text representations without introducing systematic biases or hallucinations that degrade downstream performance.
InternVid supplies 7M videos and LLM captions to train ViCLIP, which reaches leading zero-shot action recognition and competitive retrieval performance.
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| First computed | 2026-05-17T23:38:53.259393Z |
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| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7L2LCIDIL22PSJFQG4OCMYKWBU \
| jq -c '.canonical_record' \
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# expect: faf4b120685eb4f924b0371c2661560d14b994c3b8f88f9e5423bea254dd3710
Canonical record JSON
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