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20242026
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cs.CV2026

HyMobileAgent: Data-Environment Co-Scaling for Efficient GUI Agents

Hy Vision Team, Huawen Shen, Zhengyang Tang +20

As large multimodal models move from understanding content to operating on digital environments, mobile GUI has emerged as a challenging and consequential testbed for digital embod…

cs.CV2026

Manifold-Optimal Guidance: A Unified Riemannian Control View of Diffusion Guidance

Zexi Jia, Pengcheng Luo, Zhengyao Fang +2

Classifier-Free Guidance (CFG) serves as the de facto control mechanism for conditional diffusion, yet high guidance scales notoriously induce oversaturation, texture artifacts, an…

cs.CV2026

Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity

Zhengyao Fang, Zexi Jia, Yijia Zhong +5

Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authentic to real-world photography remains challe…

cs.CV2026

Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity

Zhengyao Fang, Pengyuan Lyu, Chengquan Zhang +3

Vision-language models (VLMs) face significant computational inefficiencies caused by excessive generation of visual tokens. While prior work shows that a large fraction of visual…

cs.CV2026

SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understanding

Zheng Fang, Ziwei Niu, Ziyue Wang +5

Surgical scene Multi-Task Federated Learning (MTFL) is essential for robot-assisted minimally invasive surgery (RAS) but remains underexplored in surgical video understanding due t…

cs.CV2025

Recognition-Synergistic Scene Text Editing

Zhengyao Fang, Pengyuan Lyu, Jingjing Wu +4

Scene text editing aims to modify text content within scene images while maintaining style consistency. Traditional methods achieve this by explicitly disentangling style and conte…

cs.CV2024

WeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for Transcription-only Supervised Text Spotting

Jingjing Wu, Zhengyao Fang, Pengyuan Lyu +4

Transcription-only Supervised Text Spotting aims to learn text spotters relying only on transcriptions but no text boundaries for supervision, thus eliminating expensive boundary a…