25 citations · 82 across the 70 of their papers we have counts for
75 papers
From Open Loop to Closed Loop: A Test-Time Iterative Optimization Framework for Reference-Consistent Image Generation
Baixuan Zhao, Xinyu Zhang, Huayu Zheng +4
While controllable image generation has made significant strides by incorporating visual reference conditions, existing methods predominantly operate as open-loop systems. They inj…
PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
Wei Dong, Han Zhou, Terry Ji +6
Adverse weather removal (AWR) in real-world images remains challenging due to heterogeneous and unseen degradations, while distortion-driven training often yields overly smooth res…
A2BFR: Attribute-Aware Blind Face Restoration
Chenxin Zhu, Yushun Fang, Lu Liu +5
Blind face restoration (BFR) aims to recover high-quality facial images from degraded inputs, yet its inherently ill-posed nature leads to ambiguous and uncontrollable solutions. R…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
A-Edit: Precise Reference-Guided Image Editing of Arbitrary Objects and Ambiguous Masks
Huayu Zheng, Guangzhao Li, Baixuan Zhao +4
We propose A^2-Edit, a unified inpainting framework for arbitrary object categories, which allows users to replace any target region with a reference object using only a coarse mas…
Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning
Yushuo Zheng, Huiyu Duan, Zicheng Zhang +2
Geolocation, the task of identifying the geographic location of an image, requires abundant world knowledge and complex reasoning abilities. Though advanced large multimodal models…
MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradations
Jiyao Liu, Junzhi Ning, Chenglong Ma +14
Despite impressive performance on standard benchmarks, multimodal large language models (MLLMs) face critical challenges in real-world clinical environments where medical images in…
FUMO: Prior-Modulated Diffusion for Single Image Reflection Removal
Telang Xu, Chaoyang Zhang, Guangtao Zhai +1
Single image reflection removal (SIRR) is challenging in real scenes, where reflection strength varies spatially and reflection patterns are tightly entangled with transmission str…
STAR : Bridging Statistical and Agentic Reasoning for Large Model Performance Prediction
Xiaoxiao Wang, Chunxiao Li, Junying Wang +6
As comprehensive large model evaluation becomes prohibitively expensive, predicting model performance from limited observations has become essential. However, existing statistical…
Samba+: General and Accurate Salient Object Detection via A More Unified Mamba-based Framework
Wenzhuo Zhao, Keren Fu, Jiahao He +3
Existing salient object detection (SOD) models are generally constrained by the limited receptive fields of convolutional neural networks (CNNs) and quadratic computational complex…
Embodied Image Compression
Chunyi Li, Rui Qing, Jianbo Zhang +6
Image Compression for Machines (ICM) has emerged as a pivotal research direction in the field of visual data compression. However, with the rapid evolution of machine intelligence,…
Using GUI Agent for Electronic Design Automation
Chunyi Li, Longfei Li, Zicheng Zhang +4
Graphical User Interface (GUI) agents adopt an end-to-end paradigm that maps a screenshot to an action sequence, thereby automating repetitive tasks in virtual environments. Howeve…
Consolidating Diffusion-Generated Video Detection with Unified Multimodal Forgery Learning
Xiaohong Liu, Xiufeng Song, Huayu Zheng +3
The proliferation of videos generated by diffusion models has raised increasing concerns about information security, highlighting the urgent need for reliable detection of syntheti…
GeoX-Bench: Benchmarking Cross-View Geo-Localization and Pose Estimation Capabilities of Large Multimodal Models
Yushuo Zheng, Jiangyong Ying, Huiyu Duan +5
Large multimodal models (LMMs) have demonstrated remarkable capabilities across a wide range of tasks, however their knowledge and abilities in the cross-view geo-localization and…
Q-Save: Towards Scoring and Attribution for Generated Video Evaluation
Xiele Wu, Zicheng Zhang, Mingtao Chen +12
Evaluating AI-generated video (AIGV) quality hinges on three crucial dimensions: visual quality, dynamic quality, and text-video alignment. While numerous evaluation datasets and a…
Q-REAL: Towards Realism and Plausibility Evaluation for AI-Generated Content
Shushi Wang, Zicheng Zhang, Chunyi Li +7
Quality assessment of AI-generated content is crucial for evaluating model capability and guiding model optimization. However, most existing quality assessment datasets and models…
Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding
Yi Xin, Qi Qin, Siqi Luo +29
We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by util…
AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content
Shushi Wang, Chunyi Li, Zicheng Zhang +5
AI-based image enhancement techniques have been widely adopted in various visual applications, significantly improving the perceptual quality of user-generated content (UGC). Howev…
Static and Plugged: Make Embodied Evaluation Simple
Jiahao Xiao, Jianbo Zhang, BoWen Yan +9
Embodied intelligence is advancing rapidly, driving the need for efficient evaluation. Current benchmarks typically rely on interactive simulated environments or real-world setups,…
Who is a Better Player: LLM against LLM
Yingjie Zhou, Jiezhang Cao, Farong Wen +10
Adversarial board games, as a paradigmatic domain of strategic reasoning and intelligence, have long served as both a popular competitive activity and a benchmark for evaluating ar…
Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads
Yingjie Zhou, Jiezhang Cao, Zicheng Zhang +6
Speech-driven methods for portraits are figuratively known as "Talkers" because of their capability to synthesize speaking mouth shapes and facial movements. Especially with the ra…
Lumina-mGPT 2.0: Stand-Alone AutoRegressive Image Modeling
Yi Xin, Juncheng Yan, Qi Qin +18
We present Lumina-mGPT 2.0, a stand-alone, decoder-only autoregressive model that revisits and revitalizes the autoregressive paradigm for high-quality image generation and beyond.…
NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results
Xiaohong Liu, Xiongkuo Min, Qiang Hu +92
This paper reports on the NTIRE 2025 XGC Quality Assessment Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) a…
Scaling-up Perceptual Video Quality Assessment
Ziheng Jia, Zicheng Zhang, Zeyu Zhang +12
The data scaling law has been shown to significantly enhance the performance of large multi-modal models (LMMs) across various downstream tasks. However, in the domain of perceptua…