Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations | AD Research Hub

AD Research Hub

Dashboard People Papers Workshops Datasets Research Map

⌘K

AD Research Hub — Anomaly Detection in Computer Vision

← Back to Papers

Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations

Authors

Jeonghyeon Kim, Sangheum Hwang

CVPR-2025broader adjacent

Score

4

Tags

ood

Methods

CLIPZero-shot

Datasets

ImageNet

Links

Paper Page arXiv Abstract arXiv PDF

Cite

Related Papers

OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection

Max Gutbrod, David Rauber, Danilo Weber Nunes +1

CVPR-2025close adjacent9

out-of-distributionood

OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary

Yifeng Yang, Lin Zhu, Zewen Sun +3

CVPR-2025close adjacent8

out-of-distributionood

CIFAR-10CIFAR-100

Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation

Yiping Wang, Xuehai He, Kuan Wang +5

CVPR-2025close adjacent5

ood

Blood Flow Speed Estimation with Optical Coherence Tomography Angiography Images

Wensheng Cheng, Zhenghong Li, Jiaxiang Ren +4

CVPR-2025broader adjacent4

ood

PDF

Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals

Changhao Peng

CVPR-2025broader adjacent4

ood

VAE

Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion

Jona Ballé, Luca Versari, Emilien Dupont +2

CVPR-2025broader adjacent4

ood

PDF

Sign in to access this content