110 citations · 155 across the 24 of their papers we have counts for
26 papers
LLaDA-UI: Bringing Block-wise Diffusion to Vision-Language GUI Agents
Zhangxuan Gu, Haoxing Chen, Qi Qin +17
Diffusion large language models (dLLMs) achieve high decoding efficiency through block-parallel, arbitrary-order generation, making them attractive for latency-sensitive applicatio…
LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes
Chuyan Chen, Haoxing Chen, Kun Chen +27
We introduce LLaDA-Image, a unified framework that pairs a 6B Diffusion Transformer (DiT) trained from scratch with a frozen vision-language understanding module built on the LLaDA…
UI-Venus-2 Technical Report
Venus Team, Zhuohan Cai, Haoxing Chen +28
Multimodal GUI agents have emerged as a promising paradigm for digital task automation, yet transitioning from benchmark-oriented models to dependable real-world applications remai…
MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation
Hang Yan, Zhangxuan GU, Beitong Zhou +5
Graphical user interface (GUI) agents based on large language models are increasingly deployed across mobile, web, and desktop environments. However, existing agents are typically…
VenusBench-Mobile: A Challenging and User-Centric Benchmark for Mobile GUI Agents with Capability Diagnostics
Yichen Gong, Zhuohan Cai, Sunhao Dai +4
Existing online benchmarks for mobile GUI agents remain largely app-centric and task-homogeneous, failing to reflect the diversity and instability of real-world mobile usage. To th…
UI-Venus-1.5 Technical Report
Venus Team, Changlong Gao, Zhangxuan Gu +24
GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance rem…
VenusBench-GD: A Comprehensive Multi-Platform GUI Benchmark for Diverse Grounding Tasks
Beitong Zhou, Zhexiao Huang, Yuan Guo +10
GUI grounding is a critical component in building capable GUI agents. However, existing grounding benchmarks suffer from significant limitations: they either provide insufficient d…
UI-Venus Technical Report: Building High-performance UI Agents with RFT
Zhangxuan Gu, Zhengwen Zeng, Zhenyu Xu +21
We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and…
GUI-G: Gaussian Reward Modeling for GUI Grounding
Fei Tang, Zhangxuan Gu, Zhengxi Lu +9
Graphical User Interface (GUI) grounding maps natural language instructions to precise interface locations for autonomous interaction. Current reinforcement learning approaches use…
Efficient Transfer Learning for Video-language Foundation Models
Haoxing Chen, Zizheng Huang, Yan Hong +5
Pre-trained vision-language models provide a robust foundation for efficient transfer learning across various downstream tasks. In the field of video action recognition, mainstream…
PC: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval
Yue Duan, Zhangxuan Gu, Zhenzhe Ying +3
In the realm of cross-modal retrieval, seamlessly integrating diverse modalities within multimedia remains a formidable challenge, especially given the complexities introduced by n…
E-ANT: A Large-Scale Dataset for Efficient Automatic GUI NavigaTion
Ke Wang, Tianyu Xia, Zhangxuan Gu +5
Online GUI navigation on mobile devices has driven a lot of attention recent years since it contributes to many real-world applications. With the rapid development of large languag…
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
Haoxing Chen, Yan Hong, Zizheng Huang +8
Recently, video generation techniques have advanced rapidly. Given the popularity of video content on social media platforms, these models intensify concerns about the spread of fa…
Conditional Prototype Rectification Prompt Learning
Haoxing Chen, Yaohui Li, Zizheng Huang +6
Pre-trained large-scale vision-language models (VLMs) have acquired profound understanding of general visual concepts. Recent advancements in efficient transfer learning (ETL) have…
Segment Anything Model Meets Image Harmonization
Haoxing Chen, Yaohui Li, Zhangxuan Gu +3
Image harmonization is a crucial technique in image composition that aims to seamlessly match the background by adjusting the foreground of composite images. Current methods adopt…
Boosting Audio-visual Zero-shot Learning with Large Language Models
Haoxing Chen, Yaohui Li, Yan Hong +6
Audio-visual zero-shot learning aims to recognize unseen classes based on paired audio-visual sequences. Recent methods mainly focus on learning multi-modal features aligned with c…
Backpropagation Path Search On Adversarial Transferability
Zhuoer Xu, Zhangxuan Gu, Jianping Zhang +3
Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversari…
Mobile User Interface Element Detection Via Adaptively Prompt Tuning
Zhangxuan Gu, Zhuoer Xu, Haoxing Chen +3
Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it…
DiffUTE: Universal Text Editing Diffusion Model
Haoxing Chen, Zhuoer Xu, Zhangxuan Gu +6
Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text an…
DiffusionInst: Diffusion Model for Instance Segmentation
Zhangxuan Gu, Haoxing Chen, Zhuoer Xu +3
Diffusion frameworks have achieved comparable performance with previous state-of-the-art image generation models. Researchers are curious about its variants in discriminative tasks…
Hierarchical Dynamic Image Harmonization
Haoxing Chen, Zhangxuan Gu, Yaohui Li +4
Image harmonization is a critical task in computer vision, which aims to adjust the foreground to make it compatible with the background. Recent works mainly focus on using global…
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding
Zhangxuan Gu, Changhua Meng, Ke Wang +4
Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout info…
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation
Zhengkai Jiang, Zhangxuan Gu, Jinlong Peng +6
Video Instance Segmentation (VIS) is a task that simultaneously requires classification, segmentation, and instance association in a video. Recent VIS approaches rely on sophistica…
From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic Segmentation
Zhangxuan Gu, Siyuan Zhou, Li Niu +2
Zero-shot learning has been actively studied for image classification task to relieve the burden of annotating image labels. Interestingly, semantic segmentation task requires more…