1 citations · 2 across the 8 of their papers we have counts for
8 papers
Visual Token Compression Enhances Robustness of MLLMs
Shishen Gu, Jiequan Cui, Wenbo Hu +3
In this paper, we show for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vulnerabilities such as jailbrea…
Resolving sources of uncertainty in AI weather forecasting
Wenbo Hu, Xinlei Xiong, Shuxun Zhou +5
Weather forecast uncertainty arises from imperfect analyses and forecast models, but ensemble spread alone does not reveal how distinct sources relate to downstream targets. We int…
Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning
Zijun Chen, Wenbo Hu, Richang Hong
Chain of Thought (CoT) reasoning has demonstrated remarkable deep reasoning capabilities in both large language models (LLMs) and multimodal large language models (MLLMs). However,…
Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models
Youze Wang, Wenbo Hu, Yinpeng Dong +3
Large Language Models (LLMs) have evolved into Multimodal Large Language Models (MLLMs), significantly enhancing their capabilities by integrating visual information and other type…
Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Youze Wang, Zijun Chen, Ruoyu Chen +8
Recent advancements in multimodal large language models for video understanding (videoLLMs) have enhanced their capacity to process complex spatiotemporal data. However, challenges…
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models
Zijun Chen, Wenbo Hu, Guande He +3
Multimodal large language models (MLLMs) combine visual and textual data for tasks such as image captioning and visual question answering. Proper uncertainty calibration is crucial…
Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series
Ying Liu, Peng Cui, Wenbo Hu +1
Real-world time series data often exhibits substantial missing values, posing challenges for advanced analysis. A common approach to addressing this issue is imputation, where the…
Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning
Youze Wang, Wenbo Hu, Yinpeng Dong +3
The integration of visual and textual data in Vision-Language Pre-training (VLP) models is crucial for enhancing vision-language understanding. However, the adversarial robustness…