31 citations · 32 across the 11 of their papers we have counts for
11 papers
Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models
Sandra Arcos-Holzinger, Sarah M. Erfani, James Bailey +1
Self-supervised speech models (S3Ms) achieve strong downstream performance, yet their learned representations remain poorly understood under natural and adversarial perturbations.…
Attention in Space: Functional Roles of VLM Heads for Spatial Reasoning
Xueqi Ma, Shuo Yang, Yanbei Jiang +6
Despite remarkable advances in large Vision-Language Models (VLMs), spatial reasoning remains a persistent challenge. In this work, we investigate how attention heads within VLMs c…
Semantic-aware Adversarial Fine-tuning for CLIP
Jiacheng Zhang, Jinhao Li, Hanxun Huang +3
Recent studies have shown that CLIP model's adversarial robustness in zero-shot classification tasks can be enhanced by adversarially fine-tuning its image encoder with adversarial…
Coarse-to-Fine Open-Set Graph Node Classification with Large Language Models
Xueqi Ma, Xingjun Ma, Sarah Monazam Erfani +2
Developing open-set classification methods capable of classifying in-distribution (ID) data while detecting out-of-distribution (OOD) samples is essential for deploying graph neura…
Investigating The Functional Roles of Attention Heads in Vision Language Models: Evidence for Reasoning Modules
Yanbei Jiang, Xueqi Ma, Shu Liu +5
Despite excelling on multimodal benchmarks, vision-language models (VLMs) largely remain a black box. In this paper, we propose a novel interpretability framework to systematically…
Cognitive Mirrors: Exploring the Diverse Functional Roles of Attention Heads in LLM Reasoning
Xueqi Ma, Jun Wang, Yanbei Jiang +3
Large language models (LLMs) have achieved state-of-the-art performance in a variety of tasks, but remain largely opaque in terms of their internal mechanisms. Understanding these…
Exploring Weak-to-Strong Generalization for CLIP-based Classification
Jinhao Li, Sarah M. Erfani, Lei Feng +2
Aligning large-scale commercial models with user intent is crucial to preventing harmful outputs. Current methods rely on human supervision but become impractical as model complexi…
Reasoning Like Experts: Leveraging Multimodal Large Language Models for Drawing-based Psychoanalysis
Xueqi Ma, Yanbei Jiang, Sarah Erfani +4
Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance across various objective multimodal perception tasks, yet their application to subjective, emotio…
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
Hanxun Huang, Sarah Erfani, Yige Li +2
As Contrastive Language-Image Pre-training (CLIP) models are increasingly adopted for diverse downstream tasks and integrated into large vision-language models (VLMs), their suscep…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Xingjun Ma, Yifeng Gao, Yixu Wang +45
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artifici…
Efficient Neural Implicit Representation for 3D Human Reconstruction
Zexu Huang, Sarah Monazam Erfani, Siying Lu +1
High-fidelity digital human representations are increasingly in demand in the digital world, particularly for interactive telepresence, AR/VR, 3D graphics, and the rapidly evolving…