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    <abstract>Vision-language Models (VLMs) have shown remarkable capabilities in advancing general artificial intelligence, yet the irrational encoding of visual positions persists in inhibiting the models’ comprehensive perception performance across different levels of granularity. In this work, we propose Pyramid-descent Visual Position Encoding (PyPE), a novel approach designed to enhance the perception of visual tokens within VLMs. By assigning visual position indexes from the periphery to the center and expanding the central receptive field incrementally, PyPE addresses the limitations of traditional raster-scan methods and mitigates the long-term decay effects induced by Rotary Position Embedding (RoPE). Our method reduces the relative distance between interrelated visual elements and instruction tokens, promoting a more rational allocation of attention weights and allowing for a multi-granularity perception of visual elements and countering the over-reliance on anchor tokens. Extensive experimental evaluations demonstrate that PyPE consistently improves the general capabilities of VLMs across various sizes. Code is available at https://anonymous.4open.science/r/PyPE-34EE.</abstract>
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%0 Conference Proceedings
%T Advancing General Multimodal Capability of Vision-language Models with Pyramid-descent Visual Position Encoding
%A Chen, Zhanpeng
%A Li, Mingxiao
%A Chen, Ziyang
%A Du, Nan
%A Li, Xiaolong
%A Zou, Yuexian
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chen-etal-2025-advancing
%X Vision-language Models (VLMs) have shown remarkable capabilities in advancing general artificial intelligence, yet the irrational encoding of visual positions persists in inhibiting the models’ comprehensive perception performance across different levels of granularity. In this work, we propose Pyramid-descent Visual Position Encoding (PyPE), a novel approach designed to enhance the perception of visual tokens within VLMs. By assigning visual position indexes from the periphery to the center and expanding the central receptive field incrementally, PyPE addresses the limitations of traditional raster-scan methods and mitigates the long-term decay effects induced by Rotary Position Embedding (RoPE). Our method reduces the relative distance between interrelated visual elements and instruction tokens, promoting a more rational allocation of attention weights and allowing for a multi-granularity perception of visual elements and countering the over-reliance on anchor tokens. Extensive experimental evaluations demonstrate that PyPE consistently improves the general capabilities of VLMs across various sizes. Code is available at https://anonymous.4open.science/r/PyPE-34EE.
%R 10.18653/v1/2025.findings-acl.327
%U https://aclanthology.org/2025.findings-acl.327/
%U https://doi.org/10.18653/v1/2025.findings-acl.327
%P 6324-6341

Markdown (Informal)

[Advancing General Multimodal Capability of Vision-language Models with Pyramid-descent Visual Position Encoding](https://aclanthology.org/2025.findings-acl.327/) (Chen et al., Findings 2025)

• Advancing General Multimodal Capability of Vision-language Models with Pyramid-descent Visual Position Encoding (Chen et al., Findings 2025)

ACL

• Zhanpeng Chen, Mingxiao Li, Ziyang Chen, Nan Du, Xiaolong Li, and Yuexian Zou. 2025. Advancing General Multimodal Capability of Vision-language Models with Pyramid-descent Visual Position Encoding. In Findings of the Association for Computational Linguistics: ACL 2025, pages 6324–6341, Vienna, Austria. Association for Computational Linguistics.