1 citations · 1 across the 10 of their papers we have counts for
11 papers
Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers
Yixuan Du, Chenxiao Yu, Haoyan Xu +3
Vision-Language Models (VLMs) integrate visual and textual knowledge into unified representations that increasingly underpin modern retrieval and recommendation systems. However, i…
ChromouVQA: Benchmarking Vision-Language Models under Chromatic Camouflaged Images
Yunfei Zhang, Yizhuo He, Yuanxun Shao +5
Vision-Language Models (VLMs) have advanced multimodal understanding, yet still struggle when targets are embedded in cluttered backgrounds requiring figure-ground segregation. To…
TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs
Kay Liu, Yuwei Han, Haoyan Xu +3
Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the co…
LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning
Haoyan Xu, Ruizhi Qian, Zhengtao Yao +10
Anomaly detection on attributed graphs plays an essential role in applications such as fraud detection, intrusion monitoring, and misinformation analysis. However, text-attributed…
A Systematic Study of Model Extraction Attacks on Graph Foundation Models
Haoyan Xu, Ruizhi Qian, Jiate Li +9
Graph machine learning has advanced rapidly in tasks such as link prediction, anomaly detection, and node classification. As models scale up, pretrained graph models have become va…
Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization
Langzhou He, Junyou Zhu, Fangxin Wang +5
Molecular foundation models are rapidly advancing scientific discovery, but their unreliability on out-of-distribution (OOD) samples severely limits their application in high-stake…
AMMKD: Adaptive Multimodal Multi-teacher Distillation for Lightweight Vision-Language Models
Yuqi Li, Chuanguang Yang, Junhao Dong +6
The success of large-scale visual language pretraining (VLP) models has driven widespread adoption of image-text retrieval tasks. However, their deployment on mobile devices remain…
Graph Synthetic Out-of-Distribution Exposure with Large Language Models
Haoyan Xu, Zhengtao Yao, Ziyi Wang +4
Out-of-distribution (OOD) detection in graphs is critical for ensuring model robustness in open-world and safety-sensitive applications. Existing graph OOD detection approaches typ…
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model
Haoyan Xu, Zhengtao Yao, Xuzhi Zhang +6
Out-of-distribution (OOD) detection is critical for ensuring the safety and reliability of machine learning systems, particularly in dynamic and open-world environments. In the vis…
Few-Shot Graph Out-of-Distribution Detection with LLMs
Haoyan Xu, Zhengtao Yao, Yushun Dong +4
Existing methods for graph out-of-distribution (OOD) detection typically depend on training graph neural network (GNN) classifiers using a substantial amount of labeled in-distribu…
LEGO-Learn: Label-Efficient Graph Open-Set Learning
Haoyan Xu, Kay Liu, Zhengtao Yao +4
How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to addre…