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20212025
most citedEfficient Graph Deep Learning in TensorFlow with tf_geometric

9 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2025

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…

cs.CV2025

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…

cs.MM2023★ 1 cited

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…

cs.CV2023

Iterative Adversarial Attack on Image-guided Story Ending Generation

Youze Wang, Wenbo Hu, Richang Hong

Multimodal learning involves developing models that can integrate information from various sources like images and texts. In this field, multimodal text generation is a crucial asp…

cs.LG2021★ 9 cited

Efficient Graph Deep Learning in TensorFlow with tf_geometric

Jun Hu, Shengsheng Qian, Quan Fang +4

We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries f…