7 citations · 10 across the 9 of their papers we have counts for
7 papers · 1 filter
GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding
Shijie Zhou, Viet Dac Lai, Hao Tan +4
Graphical user interface (GUI) grounding is a key capability for computer-use agents, mapping natural-language instructions to actionable regions on the screen. Existing Multimodal…
Multimodal LLMs as Customized Reward Models for Text-to-Image Generation
Shijie Zhou, Ruiyi Zhang, Huaisheng Zhu +5
We introduce LLaVA-Reward, an efficient reward model designed to automatically evaluate text-to-image (T2I) generations across multiple perspectives, leveraging pretrained multimod…
MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models
Jian Chen, Wenye Ma, Penghang Liu +7
Multimodal Large Language Models (MLLMs) have achieved remarkable visual reasoning abilities in natural images, text-rich documents, and graphic designs. However, their ability to…
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation
Shijie Zhou, Ruiyi Zhang, Yufan Zhou +1
Large multimodal models still struggle with text-rich images because of inadequate training data. Self-Instruct provides an annotation-free way for generating instruction data, but…
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
Shijie Zhou, Huaisheng Zhu, Rohan Sharma +4
Diffusion models have emerged as a powerful foundation model for visual generations. With an appropriate sampling process, it can effectively serve as a generative prior for solvin…
Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints
Jian Chen, Ruiyi Zhang, Yufan Zhou +4
Controllable layout generation refers to the process of creating a plausible visual arrangement of elements within a graphic design (e.g., document and web designs) with constraint…
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
Zhenyi Wang, Ping Yu, Yang Zhao +4
Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence…