1 citations · 1 across the 8 of their papers we have counts for
9 papers
Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation
Runsong Jia, Zhen Fang, Mengjia Wu +2
Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detect…
Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models
Yuanwei Hu, Bo Peng, Yadan Luo +3
Out-of-distribution (OOD) detection has emerged as a popular technique to enhance the reliability of machine learning models by identifying unexpected inputs from unknown classes.…
Delving into Spectral Clustering with Vision-Language Representations
Bo Peng, Yuanwei Hu, Bo Liu +3
Spectral clustering is known as a powerful technique in unsupervised data analysis. The vast majority of approaches to spectral clustering are driven by a single modality, leaving…
On the Provable Importance of Gradients for Language-Assisted Image Clustering
Bo Peng, Jie Lu, Guangquan Zhang +1
This paper investigates the recently emerged problem of Language-assisted Image Clustering (LaIC), where textual semantics are leveraged to improve the discriminability of visual r…
MiraGe: Multimodal Discriminative Representation Learning for Generalizable AI-Generated Image Detection
Kuo Shi, Jie Lu, Shanshan Ye +2
Recent advances in generative models have highlighted the need for robust detectors capable of distinguishing real images from AI-generated images. While existing methods perform w…
Learning Robust Spectral Dynamics for Temporal Domain Generalization
En Yu, Jie Lu, Xiaoyu Yang +2
Modern machine learning models struggle to maintain performance in dynamic environments where temporal distribution shifts, \emph{i.e., concept drift}, are prevalent. Temporal Doma…
A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Data
Feng Gu, Jie Lu, Zhen Fang +2
Uncertain changes in data streams present challenges for machine learning models to dynamically adapt and uphold performance in real-time. Particularly, classification boundary cha…
On the Learnability of Out-of-distribution Detection
Zhen Fang, Yixuan Li, Feng Liu +2
Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studi…
Meta OOD Learning for Continuously Adaptive OOD Detection
Xinheng Wu, Jie Lu, Zhen Fang +1
Out-of-distribution (OOD) detection is crucial to modern deep learning applications by identifying and alerting about the OOD samples that should not be tested or used for making p…