2 citations · 2 across the 32 of their papers we have counts for
37 papers
Multi-Faceted Evaluation and Mitigation of Emotion Hallucinations in MLLMs
Bowen Zeng, Peipei Song, Weidong Chen +5
Multimodal large language models (MLLMs) have shown strong potential in open-ended emotion understanding, yet they often generate emotion hallucinations. Evaluating such hallucinat…
Omni-Perception Policy Optimization for Multimodal Emotion Reasoning
Zhiyuan Han, Beier Zhu, Wenwen Tong +6
We find that current emotion-oriented Omni-MLLMs still lack reliable omni-modal perception: they (i) underutilize multimodal cues in their reasoning trajectories and (ii) exhibit u…
Robustifying Vision-Language Models via Test-Time Prompt Adaptation
Xingyu Zhu, Huanshen Wu, Shuo Wang +4
Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing tes…
Class-frequency Guided Noise Schedule for Diffusion Models
Jiequan Cui, Beier Zhu, Qingshan Xu +3
In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, l…
MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy
Zhiyuan Han, Beier Zhu, Wenwen Tong +8
We find that explicit reasoning does not necessarily translate into better multimodal emotion recognition (MER) accuracy, even though it makes predictions more interpretable. Speci…
From Uncertainty to Stability and Fidelity: Guiding Sparse-View 3D Gaussian Splatting with Fisher Information
Junbao Zhou, Qingshan Xu, Yuan Zhou +7
3D Gaussian Splatting (3DGS) has emerged as a promising technique for novel view synthesis. However, 3DGS requires dense input views to achieve high-quality rendering. In sparse-vi…
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
Xingyu Zhu, Junfeng Fang, Shuo Wang +4
Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine-tuning on annotated dat…
ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation
Bo Xu, Haotian Wu, Hehai Lin +4
Model merging aims to combine multiple task-specific expert models into a single model while preserving generalization across diverse tasks. However, interference among experts, es…
Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL
Sudong Wang, Weiquan Huang, Xiaomin Yu +9
The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiab…
Principled Steering via Null-space Projection for Jailbreak Defense in Vision-Language Models
Xingyu Zhu, Beier Zhu, Shuo Wang +4
As vision-language models (VLMs) are increasingly deployed in open-world scenarios, they can be easily induced by visual jailbreak attacks to generate harmful content, posing serio…
Adapting Point Cloud Analysis via Multimodal Bayesian Distribution Learning
Xingyu Zhu, Liang Yi, Shuo Wang +4
Multimodal 3D vision-language models show strong generalization across diverse 3D tasks, but their performance still degrades notably under domain shifts. This has motivated recent…
Few-Step Diffusion Sampling Through Instance-Aware Discretizations
Liangyu Yuan, Ruoyu Wang, Tong Zhao +4
Diffusion and flow matching models generate high-fidelity data by simulating paths defined by Ordinary or Stochastic Differential Equations (ODEs/SDEs), starting from a tractable p…
DyWeight: Dynamic Gradient Weighting for Few-Step Diffusion Sampling
Tong Zhao, Mingkun Lei, Liangyu Yuan +5
Diffusion Models (DMs) have achieved state-of-the-art generative performance across multiple modalities, yet their sampling process remains prohibitively slow due to the need for h…
NOUS: Video-Driven 3D Human Reaction Generation via Observation-Reaction Mutual Steering
Yuan Zhou, Luanyuan Dai, Yongzhi Li +7
Video-driven 3D human reaction generation aims to synthesize 3D human motion in response to the action observed in a video, playing an important role in interactive multimedia syst…
Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigation
Xingyu Zhu, Kesen Zhao, Liang Yi +4
Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language reasoning, yet they remain vulnerable to hallucination, where generated content deviat…
GuardAlign: Test-time Safety Alignment in Multimodal Large Language Models
Xingyu Zhu, Beier Zhu, Junfeng Fang +4
Large vision-language models (LVLMs) have achieved remarkable progress in vision-language reasoning tasks, yet ensuring their safety remains a critical challenge. Recent input-side…
Thinking with Images as Continuous Actions: Numerical Visual Chain-of-Thought
Kesen Zhao, Beier Zhu, Junbao Zhou +3
Recent MLLMs increasingly rely on visual chain-of-thought to perform region-grounded reasoning over images. However, existing approaches ground regions via either textified coordin…
Free Lunch for Stabilizing Rectified Flow Inversion
Chenru Wang, Beier Zhu, Chi Zhang
Rectified-Flow (RF)-based generative models have recently emerged as strong alternatives to traditional diffusion models, demonstrating state-of-the-art performance across various…
Reducing Class-Wise Performance Disparity via Margin Regularization
Beier Zhu, Kesen Zhao, Jiequan Cui +4
Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data, posing concerns for reliable deployment. While prior ef…
AMA: Adaptive Memory via Multi-Agent Collaboration
Weiquan Huang, Zixuan Wang, Hehai Lin +6
The rapid evolution of Large Language Model (LLM) agents has necessitated robust memory systems to support cohesive long-term interaction and complex reasoning. Benefiting from the…
Parallel Diffusion Solver via Residual Dirichlet Policy Optimization
Ruoyu Wang, Ziyu Li, Beier Zhu +5
Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature. Existing solver-based a…
Hierarchical Semantic Alignment for Image Clustering
Xingyu Zhu, Beier Zhu, Yunfan Li +4
Image clustering is a classic problem in computer vision, which categorizes images into different groups. Recent studies utilize nouns as external semantic knowledge to improve clu…
DEPO: Dual-Efficiency Preference Optimization for LLM Agents
Sirui Chen, Mengshi Zhao, Lei Xu +5
Recent advances in large language models (LLMs) have greatly improved their reasoning and decision-making abilities when deployed as agents. Richer reasoning, however, often comes…
Adaptive Stochastic Coefficients for Accelerating Diffusion Sampling
Ruoyu Wang, Beier Zhu, Junzhi Li +2
Diffusion-based generative processes, formulated as differential equation solving, frequently balance computational speed with sample quality. Our theoretical investigation of ODE-…