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LLaVA-Scissor introduces a training-free token compression method for video multimodal models, utilizing Semantic Connected Components for effective, non-redundant semantic coverage, outperforming existing methods in various benchmarks.
https://arxiv.org/abs//2506.21862
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
By Igor Melnyk5
33 ratings
LLaVA-Scissor introduces a training-free token compression method for video multimodal models, utilizing Semantic Connected Components for effective, non-redundant semantic coverage, outperforming existing methods in various benchmarks.
https://arxiv.org/abs//2506.21862
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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