activity
20242026
most citedPIP: Detecting Adversarial Examples in Large Vision-Language Models via Attention Patterns of Irrelevant Probe Questions

5 citations · 6 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2026

ControlRef: Efficient Layout-Guided Multi-Instance Generation via Anchored 4D-RoPE

Yunkai Yang, Yudong Zhang, Xinying Chen +7

Layout-guided multi-instance generation is essential for controllable image synthesis in Multi-Modal Diffusion Transformers (MM-DiTs). However, integrating this capability into uni…

cs.CV2026

Beyond Encoder Accumulation: Measuring Encoder Roles in Multi-Encoder VLMs

Wei Ding, Yudong Zhang, Ruobing Xie +3

As foundation models scale toward fusing more heterogeneous visual streams, understanding how diverse encoders interact under joint training becomes a prerequisite for principled d…

cs.CV2026

MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs

Wei Ding, Yilin Li, Yudong Zhang +4

Large vision-language models (LVLMs) have achieved remarkable performance across diverse multimodal tasks, yet they continue to suffer from hallucinations, generating content that…

cs.CV2025

Task-Oriented Data Synthesis and Control-Rectify Sampling for Remote Sensing Semantic Segmentation

Yunkai Yang, Yudong Zhang, Kunquan Zhang +4

With the rapid progress of controllable generation, training data synthesis has become a promising way to expand labeled datasets and alleviate manual annotation in remote sensing…

cs.LG2025

Towards a Comprehensive Scaling Law of Mixture-of-Experts

Guoliang Zhao, Yuhan Fu, Shuaipeng Li +10

Mixture-of-Experts (MoE) models have become the consensus approach for enabling parameter-efficient scaling and cost-effective deployment in large language models. However, existin…

cs.CV2025

Fighting Fire with Fire (F3): A Training-free and Efficient Visual Adversarial Example Purification Method in LVLMs

Yudong Zhang, Ruobing Xie, Yiqing Huang +5

Recent advances in large vision-language models (LVLMs) have showcased their remarkable capabilities across a wide range of multimodal vision-language tasks. However, these models…

cs.CR2025

The Security Threat of Compressed Projectors in Large Vision-Language Models

Yudong Zhang, Ruobing Xie, Xingwu Sun +4

The choice of a suitable visual language projector (VLP) is critical to the successful training of large visual language models (LVLMs). Mainstream VLPs can be broadly categorized…

cs.CV2025

QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models

Yudong Zhang, Ruobing Xie, Jiansheng Chen +3

In typical multimodal tasks, such as Visual Question Answering (VQA), adversarial attacks targeting a specific image and question can lead large vision-language models (LVLMs) to p…

cs.CV2024

Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"

Yudong Zhang, Ruobing Xie, Jiansheng Chen +3

Contrastive learning is a prevalent technique in self-supervised vision representation learning, typically generating positive pairs by applying two data augmentations to the same…

cs.CV2024★ 1 cited

DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models

Yudong Zhang, Ruobing Xie, Xingwu Sun +5

Large vision-language models (LVLMs) have demonstrated exceptional performance on complex multimodal tasks. However, they continue to suffer from significant hallucination issues,…

cs.CV2024★ 5 cited

PIP: Detecting Adversarial Examples in Large Vision-Language Models via Attention Patterns of Irrelevant Probe Questions

Yudong Zhang, Ruobing Xie, Jiansheng Chen +2

Large Vision-Language Models (LVLMs) have demonstrated their powerful multimodal capabilities. However, they also face serious safety problems, as adversaries can induce robustness…