33 citations · 69 across the 40 of their papers we have counts for
41 papers
Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders
Xin Li, Hao Jiang, Jinghao Guo +2
A recommendation request on Reddit draws several replies, each naming different titles. When an LLM recommender is trained on such threads with reinforcement learning (RL), which r…
PRISMR: Overcoming Parse Collapse in Multimodal Listwise Ranking via Parameterized Representation Internalization
Hao Jiang, Xin Li, Annan Wang +4
Generative listwise ranking with Large Multimodal Models (LMMs) aims to capture global list context in a single forward pass, but its effectiveness degrades in long-context multimo…
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning
Yuan Zhao, Youwei Pang, Jiaming Zuo +10
Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the notion of a concept remains…
R4-CGQA: Retrieval-based Vision Language Models for Computer Graphics Image Quality Assessment
Zhuangzi Li, Jian Jin, Shilv Cai +1
Immersive Computer Graphics (CGs) rendering has become ubiquitous in modern daily life. However, comprehensively evaluating CG quality remains challenging for two reasons: First, e…
TPIFM: A Task-Aware Model for Evaluating Perceptual Interaction Fluency in Remote AR Collaboration
Jiarun Song, Ninghao Wan, Fuzheng Yang +1
Remote Collaborative Augmented Reality (RCAR) enables geographically distributed users to collaborate by integrating virtual and physical environments. However, because RCAR relies…
From Perception to Cognition: How Latency Affects Interaction Fluency and Social Presence in VR Conferencing
Jiarun Song, Ninghao Wan, FuZheng Yang +1
Virtual reality (VR) conferencing has the potential to provide geographically dispersed users with an immersive environment, enabling rich social interactions and user experience u…
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…
OmniQuality-R: Advancing Reward Models Through All-Encompassing Quality Assessment
Yiting Lu, Fengbin Guan, Yixin Gao +8
Current visual evaluation approaches are typically constrained to a single task. To address this, we propose OmniQuality-R, a unified reward modeling framework that transforms mult…
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…
LMM4Edit: Benchmarking and Evaluating Multimodal Image Editing with LMMs
Zitong Xu, Huiyu Duan, Bingnan Liu +9
The rapid advancement of Text-guided Image Editing (TIE) enables image modifications through text prompts. However, current TIE models still struggle to balance image quality, edit…
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-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning
Yiting Lu, Xin Li, Haoning Wu +3
The rapid advancement of Large Multi-modal Foundation Models (LMM) has paved the way for the possible Explainable Image Quality Assessment (EIQA) with instruction tuning from two p…
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,…
Q-SiT: Teaching LMMs for Image Quality Scoring and Interpreting
Zicheng Zhang, Haoning Wu, Ziheng Jia +2
Image quality scoring and interpreting are two fundamental components of Image Quality Assessment (IQA). The former quantifies image quality, while the latter enables descriptive q…
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…
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…
Explore the Hallucination on Low-level Perception for MLLMs
Yinan Sun, Zicheng Zhang, Haoning Wu +4
The rapid development of Multi-modality Large Language Models (MLLMs) has significantly influenced various aspects of industry and daily life, showcasing impressive capabilities in…
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…
Q-Ground: Image Quality Grounding with Large Multi-modality Models
Chaofeng Chen, Sensen Yang, Haoning Wu +6
Recent advances of large multi-modality models (LMM) have greatly improved the ability of image quality assessment (IQA) method to evaluate and explain the quality of visual conten…
CMC-Bench: Towards a New Paradigm of Visual Signal Compression
Chunyi Li, Xiele Wu, Haoning Wu +7
Ultra-low bitrate image compression is a challenging and demanding topic. With the development of Large Multimodal Models (LMMs), a Cross Modality Compression (CMC) paradigm of Ima…
A-Bench: Are LMMs Masters at Evaluating AI-generated Images?
Zicheng Zhang, Haoning Wu, Chunyi Li +7
How to accurately and efficiently assess AI-generated images (AIGIs) remains a critical challenge for generative models. Given the high costs and extensive time commitments require…
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…
Dual-Branch Network for Portrait Image Quality Assessment
Wei Sun, Weixia Zhang, Yanwei Jiang +8
Portrait images typically consist of a salient person against diverse backgrounds. With the development of mobile devices and image processing techniques, users can conveniently ca…