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Conference Paper: V2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding

TitleV2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding
Authors
Ge, JunqiChen, ZiyiLin, JintaoZhu, JinguoLiu, XihuiDai, JifengZhu, Xizhou
Issue Date23-Oct-2025
Persistent Identifierhttp://hdl.handle.net/10722/359192

 

DC FieldValueLanguage
dc.contributor.authorGe, Junqi-
dc.contributor.authorChen, Ziyi-
dc.contributor.authorLin, Jintao-
dc.contributor.authorZhu, Jinguo-
dc.contributor.authorLiu, Xihui-
dc.contributor.authorDai, Jifeng-
dc.contributor.authorZhu, Xizhou-
dc.date.accessioned2025-08-23T00:30:32Z-
dc.date.available2025-08-23T00:30:32Z-
dc.date.issued2025-10-23-
dc.identifier.urihttp://hdl.handle.net/10722/359192-
dc.languageeng-
dc.relation.ispartofInternational Conference on Computer Vision (ICCV) (19/10/2025-23/10/2025, Honolulu, Hawai'i)-
dc.titleV2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding-
dc.typeConference_Paper-

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