Hugging Face Trending Papers

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

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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 %.

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arXiv Computation and Language
4d ago

GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning

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By Qingsong Xie, Jiahao Tian, Jiacheng Tang, Zhifeng Li, Madalina Ciobanu, Navan Preet Singh, Qingqing Mao, Ritankar Das
arXiv Computation and Language
Sep 17

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

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.

By Liyang Fan, Xinping Bi, Yitai Li, Shuaimin Li, Hui Li, Min Yang