A method in VideoLanguageModels (VideoLMs) enhances understanding by using codec primitives—motion vectors and residuals—in a time-ordered token sequence, improving efficiency and reducing time-to-first-token by 86%. https://arxiv.org/abs/2602.13191
CoPE-VideoLM: Leveraging Codec Primitives For Efficient Video Language Modeling
ArXiv link for CoPE-VideoLM: Leveraging Codec Primitives For Efficient Video Language Modeling
arxiv.org
April 1, 2026 at 12:20 AM
A method in VideoLanguageModels (VideoLMs) enhances understanding by using codec primitives—motion vectors and residuals—in a time-ordered token sequence, improving efficiency and reducing time-to-first-token by 86%. https://arxiv.org/abs/2602.13191
LAION just dropped a 10‑million‑hour video dataset—think massive video‑to‑text training data scraped from CommonCrawl. Perfect fuel for the next wave of video‑language models. Dive in to see what InternVid can unlock! #LAION #VideoDataset #VideoLanguageModels
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August 29, 2026 at 10:14 AM
LAION just dropped a 10‑million‑hour video dataset—think massive video‑to‑text training data scraped from CommonCrawl. Perfect fuel for the next wave of video‑language models. Dive in to see what InternVid can unlock! #LAION #VideoDataset #VideoLanguageModels
🔗 aidailypost.com/news/laion-r...
🔗 aidailypost.com/news/laion-r...
A new framework turns multiple videos into spatio‑temporal graphs, fusing them so language models can reason with richer context; tests show lower hallucination rates. Read more: https://getnews.me/structured-multi-video-reasoning-boosts-video-language-models/ #videolanguagemodels #multivideo
September 18, 2025 at 4:13 PM
A new framework turns multiple videos into spatio‑temporal graphs, fusing them so language models can reason with richer context; tests show lower hallucination rates. Read more: https://getnews.me/structured-multi-video-reasoning-boosts-video-language-models/ #videolanguagemodels #multivideo