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RankGround: Efficient High-Resolution GUI Grounding via Lightweight Reranker-Guided Crop Selection — arXiv:2609.18690

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18690
License (as archived): CC0-1.0
Archived: 2026-10-15T13:49:59.509Z
SHA-256 of the archived text: 41e0d45161ce6039278dbccb4883f99ffd6856cea0134bdfe7a00fd665cdcf65
Stable URL of this page: https://metraipolis.com/documento/dc_586df21f1e86

How to cite

RankGround: Efficient High-Resolution GUI Grounding via Lightweight Reranker-Guided Crop Selection — arXiv:2609.18690 — arXiv (en), https://arxiv.org/abs/2609.18690 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_586df21f1e86 (Metraipolis archive dc_586df21f1e86, sha256 41e0d45161ce6039278dbccb4883f99ffd6856cea0134bdfe7a00fd665cdcf65)

BibTeX

@misc{dc586df21f1e86,
  title = {RankGround: Efficient High-Resolution GUI Grounding via Lightweight Reranker-Guided Crop Selection — arXiv:2609.18690},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18690},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_586df21f1e86; sha256 41e0d45161ce6039278dbccb4883f99ffd6856cea0134bdfe7a00fd665cdcf65},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18690. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Graphical User Interface (GUI) grounding is a fundamental perception task for multimodal agents, enabling them to interpret natural language instructions and interact with digital interfaces. Existing methods face a fundamental trade-off between accuracy and efficiency: direct full-image inference often fails to capture small or visually similar UI elements, while multi-crop strategies improve localization at the cost of multiple expensive Vision-Language Model (VLM) calls per query. To address this challenge, we propose RankGround, a two-stage framework that achieves accurate GUI grounding with a single VLM call per query. Central to our approach is GroundRanker, a lightweight multimodal reranker that identifies the most promising crop from a dense candidate set. Because no off-the-shelf ranking dataset is available, we construct ranking supervision data from existing grounding datasets. A strict containment criterion and boundary-aware positive augmentation improve alignment and spatial coverage in cluttered layouts. GroundRanker is then trained with a two-stage curriculum: a pointwise objective first learns coarse containment, and a listwise objective refines subtle semantic and spatial distinctions among visually similar crops. Experimental results show that RankGround consistently outperforms strong baselines while reducing computational cost. It achieves 1.4 times faster inference and improves localization accuracy by 5.5% on average over the second-best method across all backbones and screen scales, establishing a new state of the art in both efficiency and precision for GUI grounding.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-17 in cs.CL).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Computation and Language (cs.CL); Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.18690 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T13:48:24.429Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).