1 citations · 2 across the 7 of their papers we have counts for
7 papers
Input Domain Aware MoE: Decoupling Routing Decisions from Task Optimization in Mixture of Experts
Yongxiang Hua, Haoyu Cao, Zhou Tao +4
Sparse Mixture of Experts (sMoE) has become a pivotal approach for scaling large vision-language models, offering substantial capacity while maintaining computational efficiency th…
FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing
Shida Wang, Chaohu Liu, Yubo Wang +1
Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable intellectual assets. Nevertheless, the…
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization
Chaohu Liu, Tianyi Gui, Yu Liu +1
Large Vision-Language Models (LVLMs), such as GPT-4o and LLaVA, have recently witnessed remarkable advancements and are increasingly being deployed in real-world applications. Howe…
Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Images
Yubo Wang, Jianting Tang, Chaohu Liu +1
Large vision-language models (LVLMs) have demonstrated remarkable image understanding and dialogue capabilities, allowing them to handle a variety of visual question answering task…
Break the Visual Perception: Adversarial Attacks Targeting Encoded Visual Tokens of Large Vision-Language Models
Yubo Wang, Chaohu Liu, Yanqiu Qu +3
Large vision-language models (LVLMs) integrate visual information into large language models, showcasing remarkable multi-modal conversational capabilities. However, the visual mod…
HRVDA: High-Resolution Visual Document Assistant
Chaohu Liu, Kun Yin, Haoyu Cao +6
Leveraging vast training data, multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance a…
Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration
Haoyu Cao, Changcun Bao, Chaohu Liu +6
We propose a novel end-to-end document understanding model called SeRum (SElective Region Understanding Model) for extracting meaningful information from document images, including…