← Back to all news
arXiv Computation and Language By Liyang Fan, Xinping Bi, Yitai Li, Shuaimin Li, Hui Li, Min Yang

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

Read the original on arXiv Computation and Language →

RankGround is a two‑stage framework for GUI grounding that uses a single Vision‑Language Model call per query. It introduces GroundRanker, a lightweight multimodal reranker that selects the most promising crop from a dense candidate set, trained with a two‑stage curriculum on ranking supervision data derived from existing grounding datasets. Experiments show RankGround outperforms strong baselines, achieving 1.4× faster inference and a 5.5% average improvement in localization accuracy over the second‑best method across all backbones and screen scales.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Computer Vision
1d ago

GUI-Lens: Enabling Precise GUI Grounding for Off-the-Shelf VLMs via Coarse-to-Fine Cropping

arXiv:2608.03270v2 Announce Type: replace Abstract: GUI grounding maps natural-language instructions to click locations and is essential for reliable GUI agents. The task remains difficult on high-re...

By Zichuan Fu, Shirong Wang, Wenlin Zhang, Guojing Li, Yimin Deng, Jingtong Gao, Junjia Qi, Hanyu Yan, Yefeng Zheng, Xiaopeng Li, Wanyu Wang, Xian Wu, Xiangyu Zhao
llmsagentscomputer-visionmultimodalbenchmarks
More like this →
Hugging Face Trending Papers
Oct 13

MHVF: Training-Free GUI Grounding via Verification-Guided Multi-Hypothesis Search

The paper introduces MHVF, a training‑free framework for GUI grounding that uses multi‑hypothesis exploration and local visual verification with a single pretrained vision‑language model. MHVF generates spatial hypotheses from full‑screen predictions, refines them through zoomed re‑localization, and verifies candidate agreement with the instruction to resolve ambiguity. Experiments on eight benchmarks with five models show significant accuracy gains, notably improving UI‑Venus‑Ground‑7B by 4.3 pp on Mind2Web and raising Qwen2.5‑VL‑7B’s ScreenSpot‑Pro accuracy from 23.6 % to 53.6 %.

llmsagentsmultimodalbenchmarks
More like this →
arXiv Computer Vision
Sep 18

AgriScope: Pixel-Grounded Multimodal Understanding for Agricultural Images

AgriScope is a unified pixel‑grounded multimodal framework designed for agricultural image understanding. It supports image‑level, region‑level, and pixel‑level tasks such as grounded caption generation, referring expression segmentation, and multi‑turn multimodal interaction. The authors also introduce AgriGround, a large‑scale dataset with over 500K images and 11M instruction‑following samples, created via an automatic annotation pipeline that combines caption generation, phrase‑level grounding, segmentation mask creation, and instruction synthesis.

By Abderrahmene Boudiaf, Mohamad Alanssari, Irfan Hussain, Sajid Javed
llmscomputer-visionmultimodalbenchmarks
More like this →