5 papers
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.…
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…
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…
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…