AD Research Hub
DashboardPeoplePapersWorkshopsDatasetsResearch Map
Login
⌘K
AD Research Hub — Anomaly Detection in Computer Vision
← Back to Papers
Forensic Self-Descriptions Are All You Need for Zero-Shot Detection, Open-Set Source Attribution, and Clustering of AI-generated Images

Authors

Tai D. Nguyen, Aref Azizpour, Matthew C. Stamm

CVPR-2025broader adjacent

Score

4

Tags

open-set

Methods

Zero-shot

Links

Paper PagearXiv AbstractarXiv PDF

Cite

Related Papers

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection

Fuyun Wang, Tong Zhang, Yuanzhi Wang +4

CVPR-2025direct anomaly17
anomaly detectionopen-set
PDFarXiv

Multi-subject Open-set Personalization in Video Generation

Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace +7

CVPR-2025close adjacent5
open-set
CLIP
PDFarXiv

CLIP-driven Coarse-to-fine Semantic Guidance for Fine-grained Open-set Semi-supervised Learning

Xiaokun Li, Yaping Huang, Qingji Guan

CVPR-2025broader adjacent4
open-set
CLIP
PDF

Cross-Rejective Open-Set SAR Image Registration

Shasha Mao, Shiming Lu, Zhaolong Du +6

CVPR-2025broader adjacent4
open-set
PDF

ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models

Yahan Tu, Rui Hu, Jitao Sang

CVPR-2025broader adjacent4
open-set
PDFarXiv

Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach

Chen-Chen Zong, Sheng-Jun Huang

CVPR-2025broader adjacent4
open-set
PDFarXiv

Sign in to access this content

Sign in