GUI 定位

已审阅并标注「GUI 定位」的研究与资产。

条目依据已审阅标签归类,同一主题不表示资源之间存在依赖关系。日期保留来源精度,注释保留原语言。

模型

GUI-Actor 7B Qwen2-VL

GUI-Actor checkpoint fine-tuned from Qwen2-VL-7B-Instruct, with about 8.4B BF16 parameters, whose config names a pointer variant of the Qwen2-VL class rather than the stock one, under MIT.

跨平台
模型

GUI-G1

GUI-grounding training code that modifies reasoning templates, reward functions, and the GRPO objective, with a linked model release.

跨平台
模型

GUI-G1 3B v1

A Qwen2.5-VL-3B checkpoint released for R1-style visual grounding experiments, predicting interface target locations from screenshots and instructions.

跨平台
模型

GUI-G2

GUI grounding models at 3B and 7B trained with Gaussian rewards for click proximity to element centers and region coverage, with variance scaled to element size.

跨平台
模型

GUI-G2 7B

A Qwen2.5-VL grounding checkpoint trained with Gaussian point and coverage rewards whose granularity adapts to interface-element scale.

跨平台
模型

GUI-Owl 7B

A Qwen2.5-VL-based GUI checkpoint released with Mobile-Agent-v3 for grounded actions and specialized roles in its agent framework.

Android桌面
模型

GUI-R1

GUI action-model training and evaluation code with verifiable rewards, plus linked reinforcement-learning data and checkpoints.

跨平台
模型

Holo1 7B

A Qwen2.5-VL-based action model released for the Surfer-H web-agent system, supporting policy, interface-localization and validation roles.

网页
模型

Holo2 4B

A Qwen3-VL-based checkpoint for navigation and multi-step GUI actions across web, desktop and mobile interfaces.

网页桌面跨平台
模型

InfiGUI-G1

InfiGUI-G1 3B and 7B grounding models trained with Adaptive Exploration Policy Optimization, which samples several candidate answers per pass under an adaptive exploration reward.

跨平台
模型

InfiGUI-G1 7B

A GUI-grounding checkpoint trained with adaptive exploration rewards and multiple candidate answers to improve instruction-to-element alignment.

跨平台
模型

InfiGUI-R1 3B

A Qwen2.5-VL checkpoint trained with the Actor2Reasoner procedure to combine interface grounding with planning and recovery reasoning.

网页桌面Android
模型

Magma

Microsoft foundation model for multimodal agents, pretrained with Set-of-Mark and Trace-of-Mark objectives on UI, robotics and video data and fine-tunable for web and mobile UI navigation.

网页
模型

OpenCUA 32B

A Qwen2.5-VL-based computer-use checkpoint that combines screenshot history with planning and grounded executable actions.

桌面LinuxWindowsmacOS
模型

OpenCUA 72B

A Qwen2.5-VL-based computer-use checkpoint that combines screenshot history and reflective reasoning to predict executable desktop actions.

桌面LinuxWindowsmacOS
模型

OS-Atlas

OS-Atlas GUI action models, including grounding bases fine-tuned from InternVL2-4B and Qwen2-VL-7B-Instruct that accept any image size and output 0-1000 relative coordinates.

跨平台
模型

Phi-Ground

Microsoft GUI grounding models from an empirical study of grounding training, from data collection to model training, released as Phi-Ground-4B-7C with evaluation code.

跨平台
模型

Phi-Ground checkpoint

Microsoft Phi-Ground GUI grounding checkpoint fine-tuned from Phi-3.5-vision-instruct, shipped as two PyTorch weight shards with custom Phi-3-V configuration and processing code under MIT.

跨平台
模型

POINTS-GUI

POINTS-GUI-G-8B GUI grounding model, trained from a POINTS-1.5 base without native grounding through a data pipeline that unifies coordinates, filters noise and adds hard cases.

跨平台
模型

ScaleCUA 7B

A Qwen2.5-VL-based computer-use checkpoint with direct grounding and agent action modes across desktop, mobile and web interfaces.

网页桌面AndroidLinux
模型

ShowUI

Lightweight GUI-agent vision-language-action model from Show Lab and Microsoft, released with UI-guided token selection, training code, the ShowUI-web and ShowUI-desktop datasets and vLLM inference.

跨平台
模型

ShowUI 2B

ShowUI vision-language-action checkpoint fine-tuned from Qwen2-VL-2B-Instruct, published as a single PyTorch weight file under the MIT license and tagged for GUI agents and computer use.

跨平台
模型

ShowUI-π

Vision-language-action model and training code for GUI dragging, with links to checkpoints and continuous-action evaluation data.

桌面
模型

UGround

Code, training data and weights for universal visual grounding of GUI elements from screenshots, including Qwen2-VL-based UGround-V1 at 2B, 7B and 72B and offline and online evaluation suites.

跨平台
模型

UGround V1 7B

UGround-V1 checkpoint fine-tuned from Qwen2-VL-7B, with about 8.3B BF16 parameters in the standard Qwen2-VL architecture, provided as safetensors and a PyTorch file under Apache-2.0.

跨平台
模型

UI-AGILE

GUI agent trained by RL with a continuous reward, a simple-thinking reward and cropping-based resampling on about 9k samples, plus a plug-in decomposed grounding-and-selection inference method.

跨平台
模型

UI-R1

Rule-based reinforcement learning for GUI action prediction, releasing UI-R1-3B, trained from Qwen2.5-VL-3B, the efficiency-oriented UI-R1-E-3B, and training data.

跨平台
模型

UI-R1 3B

A Qwen2.5-VL checkpoint trained with reinforcement learning for GUI action prediction and evaluated on mobile, desktop and web grounding tasks.

网页桌面Android
模型

UI-TARS

Native GUI agent models; UI-TARS-1.5 adds reinforcement-learned reasoning before acting and an open 7B checkpoint, and UI-TARS-2 extends to games, code and tool use.

跨平台
模型

UI-TARS 1.5 7B

UI-TARS 1.5 GUI-agent checkpoint in the Qwen2.5-VL architecture with about 8.3B parameters stored in FP32, released under Apache-2.0 for English image-text-to-text use in Transformers.

跨平台
模型

UI-Venus

GUI agent model family; UI-Venus-2, at 9B and 27B, acts on mobile, web and desktop in one end-to-end agent, trained with keypoint-based multi-judge task verification.

跨平台
模型

UI-Venus 2 9B

UI-Venus 2 GUI-agent checkpoint fine-tuned from Qwen3.5-9B, with about 9.4B BF16 parameters in the Qwen3.5 multimodal architecture, loaded through Transformers for image-text-to-text.

跨平台
模型

ZonUI-3B checkpoint

Qwen2.5-VL-3B-based GUI grounding checkpoint that predicts click locations from screenshots and text instructions across desktop, mobile and web screens.

跨平台桌面网页

工程与产业

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