2 citations · 2 across the 14 of their papers we have counts for
17 papers
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…
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…
EvalTalker: Learning to Evaluate Real-Portrait-Driven Multi-Subject Talking Humans
Yingjie Zhou, Xilei Zhu, Siyu Ren +11
Speech-driven Talking Human (TH) generation, commonly known as "Talker," currently faces limitations in multi-subject driving capabilities. Extending this paradigm to "Multi-Talker…
Q-Mirror: Unlocking the Multi-Modal Potential of Scientific Text-Only QA Pairs
Junying Wang, Zicheng Zhang, Ye Shen +8
High-quality, multi-modal benchmarks are crucial for advancing scientific reasoning in large models yet their manual creation is costly and unscalable. To address this bottleneck,…
A Multi-To-One Interview Paradigm for Efficient MLLM Evaluation
Ye Shen, Junying Wang, Farong Wen +4
The rapid progress of Multi-Modal Large Language Models (MLLMs) has spurred the creation of numerous benchmarks. However, conventional full-coverage Question-Answering evaluations…
QoNext: Towards Next-generation QoE for Foundation Models
Yijin Guo, Farong Wen, Ye Shen +5
Existing evaluations of foundation models predominantly focus on output correctness, treating interaction as a static exchange of information. However, such perspectives overlook t…
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…
The Ever-Evolving Science Exam
Junying Wang, Zicheng Zhang, Yijin Guo +9
As foundation models grow rapidly in capability and deployment, evaluating their scientific understanding becomes increasingly critical. Existing science benchmarks have made progr…
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…
Research-Oriented Human-Centric Evaluation for Foundation Models
Yijin Guo, Kaiyuan Ji, Xiaorong Zhu +5
Most current evaluations of foundation models focus on objective benchmarks, such as knowledge coverage and reasoning accuracy, often overlooking users' subjective experiences in h…
Improve MLLM Benchmark Efficiency through Interview
Farong Wen, Yijin Guo, Junying Wang +6
The rapid development of Multimodal Large Language Models (MLLM) has led to a wide range of MLLM applications, and a number of benchmark datasets have sprung up in order to assess…
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…
Q-Agent: Quality-Driven Chain-of-Thought Image Restoration Agent through Robust Multimodal Large Language Model
Yingjie Zhou, Jiezhang Cao, Farong Wen +7
Image restoration (IR) often faces various complex and unknown degradations in real-world scenarios, such as noise, blurring, compression artifacts, and low resolution, etc. Traini…
Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM
Xinyu Fang, Zhijian Chen, Kai Lan +10
Creativity is a fundamental aspect of intelligence, involving the ability to generate novel and appropriate solutions across diverse contexts. While Large Language Models (LLMs) ha…
MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis
Yingjie Zhou, Zicheng Zhang, Jiezhang Cao +6
Artificial Intelligence (AI) has demonstrated significant capabilities in various fields, and in areas such as human-computer interaction (HCI), embodied intelligence, and the desi…
3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents
Yingjie Zhou, Zicheng Zhang, Farong Wen +5
Although 3D generated content (3DGC) offers advantages in reducing production costs and accelerating design timelines, its quality often falls short when compared to 3D professiona…