3 citations · 8 across the 19 of their papers we have counts for
19 papers
Do VLMs Share Safety Neurons Across Modalities?
Jiaxuan Li, Jiahao Zhang, Duc Minh Vo +3
Vision-language models (VLMs) can comply with harmful requests delivered through images, even when their LLM backbones would refuse the same content in text. While prior work chara…
EDGE-Shield: Efficient Denoising-staGE Shield for Violative Content Filtering via Scalable Reference-Based Matching
Takara Taniguchi, Ryohei Shimizu, Duc Minh Vo +3
The advent of Text-to-Image generative models poses significant risks of copyright violation and deepfake generation. Since the rapid proliferation of new copyrighted works and pri…
Comparing verbal, visual and combined explanations for Bayesian Network inferences
Erik P. Nyberg, Steven Mascaro, Ingrid Zukerman +3
Bayesian Networks (BNs) are an important tool for assisting probabilistic reasoning, but despite being considered transparent models, people have trouble understanding them. Furthe…
R.I.P.: A Simple Black-box Attack on Continual Test-time Adaptation
Trung-Hieu Hoang, Duc Minh Vo, Minh N. Do
Test-time adaptation (TTA) has emerged as a promising solution to tackle the continual domain shift in machine learning by allowing model parameters to change at test time, via sel…
NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects?
Jiaxuan Li, Junwen Mo, MinhDuc Vo +2
Multimodal Large Language Models (MLLMs) have made notable advances in visual understanding, yet their abilities to recognize objects modified by specific attributes remain an open…
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input
Trung-Hieu Hoang, Mona Zehni, Huy Phan +2
Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their generalization on challenging in-the-wild videos remain an open…