5 citations · 6 across the 9 of their papers we have counts for
9 papers
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
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…
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…
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…
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…
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…
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…
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,…
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…