1 citations · 1 across the 9 of their papers we have counts for
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UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations
Zihan Ding, Longxu Dou, Qi Gao +26
Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution. Routine workfl…
SteerVTE: Seamless Video Text Editing with Style and Glyph Control
Kai Zeng, Moran Li, Zhengwei Wang +6
Visual text editing aims to precisely modify text in images and videos while preserving stylistic consistency and visual realism. Despite significant advances in the image domain,…
TextSculptor: Training and Benchmarking Scene Text Editing
Yiheng Lin, Siyu Jiao, Xiaohan Lan +12
Recent advances in Multimodal Large Language Models (MLLMs) and diffusion-based generative models have substantially improved prompt-driven image editing. However, scene text editi…
Let ViT Speak: Generative Language-Image Pre-training
Yan Fang, Mengcheng Lan, Zilong Huang +7
In this paper, we present \textbf{Gen}erative \textbf{L}anguage-\textbf{I}mage \textbf{P}re-training (GenLIP), a minimalist generative pretraining framework for Vision Transformers…
Monocular Normal Estimation via Shading Sequence Estimation
Zongrui Li, Xinhua Ma, Minghui Hu +6
Monocular normal estimation aims to estimate the normal map from a single RGB image of an object under arbitrary lights. Existing methods rely on deep models to directly predict no…