40 citations · 111 across the 26 of their papers we have counts for
26 papers
CertVLA: Certified Defense against Physical Visual Attacks for Vision-Language-Action Models
Hui Lu, Zhijie Peng, Yuqi Lin +8
Vision-Language-Action (VLA) policies are vulnerable to localized physical perturbations, yet existing certified patch defenses target discrete labels and cannot directly certify c…
ChronoLock: Protecting Videos from Unauthorized Text-to-Video Personalization
Jiaming He, Jiashu Zhang, Guanyu Hou +4
Text-to-video (T2V) diffusion models have made it increasingly easy to synthesize realistic and temporally coherent videos, while recent personalization techniques allow such model…
Q-Tacit: Image Quality Assessment via Latent Visual Reasoning
Yuxuan Jiang, Yixuan Li, Hanwei Zhu +3
Vision-Language Model (VLM)-based image quality assessment (IQA) has been significantly advanced by incorporating Chain-of-Thought (CoT) reasoning. Recent work has refined image qu…
EduVQA: Towards Concept-Aware Assessment of Educational AI-Generated Videos
Baoliang Chen, Xinlong Bu, Hanwei Zhu +2
Existing AI-generated video quality assessment (AIGVQA) methods mainly focus on global perceptual realism and coarse text-video alignment, while overlooking a critical requirement…
Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings
Zihao Lin, Zhenshan Shi, Sasa Zhao +4
Assessing human creativity through visual outputs, such as drawings, plays a critical role in fields including psychology, education, and cognitive science. However, current assess…
Q-Doc: Benchmarking Document Image Quality Assessment Capabilities in Multi-modal Large Language Models
Jiaxi Huang, Dongxu Wu, Hanwei Zhu +4
The rapid advancement of Multi-modal Large Language Models (MLLMs) has expanded their capabilities beyond high-level vision tasks. Nevertheless, their potential for Document Image…
Diagnosing Corruption-Induced Reliability Failures in Vision-Language Models
Xiangjie Sui, Songyang Li, Hanwei Zhu +3
Visual corruptions can change vision--language model (VLM) behavior in ways that top-1 accuracy does not capture. A model may keep the same answer while losing distributional suppo…
Beyond Cosine Similarity: Magnitude-Aware CLIP for No-Reference Image Quality Assessment
Zhicheng Liao, Dongxu Wu, Zhenshan Shi +5
Recent efforts have repurposed the Contrastive Language-Image Pre-training (CLIP) model for No-Reference Image Quality Assessment (NR-IQA) by measuring the cosine similarity betwee…
VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Results
Hanwei Zhu, Haoning Wu, Zicheng Zhang +26
This paper presents a summary of the VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models (LMMs), hosted as part of the ICCV 2025 Workshop on Visual Quali…
Compressing Human Body Video with Interactive Semantics: A Generative Approach
Bolin Chen, Shanzhi Yin, Hanwei Zhu +6
In this paper, we propose to compress human body video with interactive semantics, which can facilitate video coding to be interactive and controllable by manipulating semantic-lev…
Pleno-Generation: A Scalable Generative Face Video Compression Framework with Bandwidth Intelligence
Bolin Chen, Hanwei Zhu, Shanzhi Yin +5
Generative model based compact video compression is typically operated within a relative narrow range of bitrates, and often with an emphasis on ultra-low rate applications. There…
AI-generated Image Quality Assessment in Visual Communication
Yu Tian, Yixuan Li, Baoliang Chen +3
Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality asse…
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment
Baoliang Chen, Siyi Pan, Dongxu Wu +4
Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason…
Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement
Lingyu Zhu, Wenhan Yang, Baoliang Chen +4
Obtaining pairs of low/normal-light videos, with motions, is more challenging than still images, which raises technical issues and poses the technical route of unpaired learning as…
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
Hanwei Zhu, Haoning Wu, Yixuan Li +7
While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to…
Towards Open-ended Visual Quality Comparison
Haoning Wu, Hanwei Zhu, Zicheng Zhang +11
Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardi…
2AFC Prompting of Large Multimodal Models for Image Quality Assessment
Hanwei Zhu, Xiangjie Sui, Baoliang Chen +4
While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessmen…
Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation
Yixuan Li, Peilin Chen, Hanwei Zhu +3
Opinion-Unaware Blind Image Quality Assessment (OU-BIQA) models aim to predict image quality without training on reference images and subjective quality scores. Thereinto, image st…
Perceptual Quality Assessment of 360 Images Based on Generative Scanpath Representation
Xiangjie Sui, Hanwei Zhu, Xuelin Liu +3
Despite substantial efforts dedicated to the design of heuristic models for omnidirectional (i.e., 360) image quality assessment (OIQA), a conspicuous gap remains due to th…
Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement
Baoliang Chen, Lingyu Zhu, Hanwei Zhu +3
There is a growing consensus in the research community that the optimization of low-light image enhancement approaches should be guided by the visual quality perceived by end users…
DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
Hanwei Zhu, Baoliang Chen, Lingyu Zhu +2
ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. Such a remarkable byproduct has often be…
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
Baoliang Chen, Hanwei Zhu, Lingyu Zhu +2
The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widel…
DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
Xingran Liao, Baoliang Chen, Hanwei Zhu +3
Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely…
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
Danni Huang, Lingyu Zhu, Zihao Lin +3
There is an increasing consensus that the design and optimization of low light image enhancement methods need to be fully driven by perceptual quality. With numerous approaches pro…