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New Hi-Token method enhances visual grounding accuracy in AI models

By PulseAugur Editorial · [3 sources] ·

Researchers have developed Hi-Token, a novel method for generative visual grounding that improves the accuracy of bounding-box predictions by tokenizing coordinates hierarchically. This approach encodes digits for hundreds, tens, and ones, adding structure and promoting token reuse within vision-language models. Complementary to Hi-Token, Hi-GAR uses a geometry-based reward during training to further refine localization accuracy. Experiments across multiple vision-language model backbones and benchmarks demonstrate consistent performance gains, with Hi-R1 achieving superior results compared to existing specialist methods. AI

IMPACT Improves localization accuracy in AI models for visual grounding tasks, potentially enhancing applications that require precise object identification from text descriptions.

RANK_REASON The cluster contains two arXiv papers detailing new research and surveys in the field of visual grounding.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New Hi-Token method enhances visual grounding accuracy in AI models

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The cluster contains two arXiv papers detailing new research and surveys in the field of visual grounding.
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COVERAGE [3]

  1. arXiv cs.CV TIER_1 English(EN) · Haijie Li, Jiaxin Zhang, Dave Zhenyu Chen, Youyu Chen, Yanmin Wu, Jian Zhang ·

    CoordRefer: Coordinate-Aware 3D Visual Grounding from Multiview Images

    arXiv:2608.05569v1 Announce Type: new Abstract: Multiview image-based 3D visual grounding predicts a coordinate frame to define a coordinate system and then regresses a 3D bounding box for localization. However, existing methods jointly optimize coordinate frame selection and box…

  2. arXiv cs.CV TIER_1 English(EN) · Xiuyuan Zhu, Ke Lu, Kun Dong, Siwen Jiao, Hao Wu, Zijin Du, Shun Mao, Dongming Zhang, Jian Xue ·

    Hi-Token: Hierarchical Coordinate Tokenization for Generative Visual Grounding

    arXiv:2608.03471v1 Announce Type: new Abstract: Generative Vision-Language Models (VLMs) commonly treat bounding-box coordinates as independent output symbols, leaving numerical order and axis semantics implicit. We identify this representation as an important source of error in …

  3. arXiv cs.CV TIER_1 English(EN) · Linhui Xiao, Xiaoshan Yang, Xiangyuan Lan, Yaowei Wang, Changsheng Xu ·

    Toward Visual Grounding: A Survey

    arXiv:2412.20206v4 Announce Type: replace Abstract: Visual Grounding, also known as Referring Expression Comprehension and Phrase Grounding, aims to ground the specific region(s) within the image(s) based on the given expression text. This task simulates the common referential re…

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