33 citations · 93 across the 56 of their papers we have counts for
42 papers · 1 filter
Towards Characterizing Scientific Image Utility and Upgradability
WenZhe Li, Qihang Yan, Liang Chen +6
Scientific images function as critical evidence in research communication, yet their integrity faces unprecedented threats from AI-generated content that introduces subtle but cons…
GeoR-Bench: Evaluating Geoscience Visual Reasoning
Yushuo Zheng, Zicheng Zhang, Huiyu Duan +7
Geoscience intelligence is expected to understand, reason about, and predict earth system changes to support human decision-making in critical domains such as disaster response, cl…
SIQA: Toward Reliable Scientific Image Quality Assessment
Wenzhe Li, Liang Chen, Junying Wang +6
Scientific images fundamentally differ from natural and AI-generated images in that they encode structured domain knowledge rather than merely depict visual scenes. Assessing their…
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…
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-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…
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…
Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification
Yuan Tian, Shuo Wang, Rongzhao Zhang +8
Medical imaging has significantly advanced computer-aided diagnosis, yet its re-identification (ReID) risks raise critical privacy concerns, calling for de-identification (DeID) te…
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…
Image Quality Assessment for Embodied AI
Chunyi Li, Jiaohao Xiao, Jianbo Zhang +8
Embodied AI has developed rapidly in recent years, but it is still mainly deployed in laboratories, with various distortions in the Real-world limiting its application. Traditional…
PuzzleBench: A Fully Dynamic Evaluation Framework for Large Multimodal Models on Puzzle Solving
Zeyu Zhang, Zijian Chen, Zicheng Zhang +7
Large Multimodal Models (LMMs) have demonstrated impressive capabilities across a wide range of multimodal tasks, achieving ever-increasing performance on various evaluation benchm…
Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing
Ruiyi Wang, Yushuo Zheng, Zicheng Zhang +4
Existing real-world image dehazing methods primarily attempt to fine-tune pre-trained models or adapt their inference procedures, thus heavily relying on the pre-trained models and…
Image Quality Assessment: From Human to Machine Preference
Chunyi Li, Yuan Tian, Xiaoyue Ling +9
Image Quality Assessment (IQA) based on human subjective preferences has undergone extensive research in the past decades. However, with the development of communication protocols,…
Towards All-in-One Medical Image Re-Identification
Yuan Tian, Kaiyuan Ji, Rongzhao Zhang +4
Medical image re-identification (MedReID) is under-explored so far, despite its critical applications in personalized healthcare and privacy protection. In this paper, we introduce…
Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content
Zicheng Zhang, Tengchuan Kou, Shushi Wang +9
Evaluating text-to-vision content hinges on two crucial aspects: visual quality and alignment. While significant progress has been made in developing objective models to assess the…
A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment
Bo Hu, Wei Wang, Chunyi Li +3
Wide-angle video is favored for its wide viewing angle and ability to capture a large area of scenery, making it an ideal choice for sports and adventure recording. However, wide-a…
VQA: Visual Question Answering for Video Quality Assessment
Ziheng Jia, Zicheng Zhang, Jiaying Qian +7
The advent and proliferation of large multi-modal models (LMMs) have introduced new paradigms to computer vision, transforming various tasks into a unified visual question answerin…
Embodied Image Quality Assessment for Robotic Intelligence
Jianbo Zhang, Chunyi Li, Jie Hao +5
Image Quality Assessment (IQA) of User-Generated Content (UGC) is a critical technique for human Quality of Experience (QoE). However, does the the image quality of Robot-Generated…
R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?
Chunyi Li, Jianbo Zhang, Zicheng Zhang +8
The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images…
Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs
Zicheng Zhang, Ziheng Jia, Haoning Wu +8
With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting t…
Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model
Zhichao Zhang, Wei Sun, Xinyue Li +9
In recent years, artificial intelligence (AI)-driven video generation has gained significant attention. Consequently, there is a growing need for accurate video quality assessment…
HazeCLIP: Towards Language Guided Real-World Image Dehazing
Ruiyi Wang, Wenhao Li, Xiaohong Liu +4
Existing methods have achieved remarkable performance in image dehazing, particularly on synthetic datasets. However, they often struggle with real-world hazy images due to domain…