4 citations · 7 across the 16 of their papers we have counts for
15 papers · 1 filter
A: Towards Advertising Aesthetic Assessment
Kaiyuan Ji, Yixuan Gao, Lu Sun +7
Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized…
MACEval: A Multi-Agent Continual Evaluation Network for Large Models
Zijian Chen, Yuze Sun, Yuan Tian +2
Hundreds of benchmarks dedicated to evaluating large models have been presented over the past few years. However, most of them remain closed-ended and are prone to overfitting due…
ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer
Yuan Tian, Min Zhou, Yitong Chen +19
Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection fr…
Can Large Models Fool the Eye? A New Turing Test for Biological Animation
Zijian Chen, Lirong Deng, Zhengyu Chen +5
Evaluating the abilities of large models and manifesting their gaps are challenging. Current benchmarks adopt either ground-truth-based score-form evaluation on static datasets or…
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…
Just Noticeable Difference for Large Multimodal Models
Zijian Chen, Yuan Tian, Yuze Sun +5
Just noticeable difference (JND), the minimum change that the human visual system (HVS) can perceive, has been studied for decades. Although recent work has extended this line of r…
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…
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…
Medical Manifestation-Aware De-Identification
Yuan Tian, Shuo Wang, Guangtao Zhai
Face de-identification (DeID) has been widely studied for common scenes, but remains under-researched for medical scenes, mostly due to the lack of large-scale patient face dataset…
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…
Free-VSC: Free Semantics from Visual Foundation Models for Unsupervised Video Semantic Compression
Yuan Tian, Guo Lu, Guangtao Zhai
Unsupervised video semantic compression (UVSC), i.e., compressing videos to better support various analysis tasks, has recently garnered attention. However, the semantic richness o…
GAIA: Rethinking Action Quality Assessment for AI-Generated Videos
Zijian Chen, Wei Sun, Yuan Tian +7
Assessing action quality is both imperative and challenging due to its significant impact on the quality of AI-generated videos, further complicated by the inherently ambiguous nat…
SMC++: Masked Learning of Unsupervised Video Semantic Compression
Yuan Tian, Xiaoyue Ling, Cong Geng +3
Most video compression methods focus on human visual perception, neglecting semantic preservation. This leads to severe semantic loss during the compression, hampering downstream v…