Saved in:
| Main Authors: | Liu, Xinyue, Wang, Jianyuan, Leng, Biao, Zhang, Shuo |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.08949 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Unlocking the Potential of Reverse Distillation for Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2024)
by: Liu, Xinyue, et al.
Published: (2024)
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2024)
by: Liu, Xinyue, et al.
Published: (2024)
Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2025)
by: Liu, Xinyue, et al.
Published: (2025)
Res$^2$CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment
by: Liu, Xinyue, et al.
Published: (2026)
by: Liu, Xinyue, et al.
Published: (2026)
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
by: Costanzino, Alex, et al.
Published: (2023)
by: Costanzino, Alex, et al.
Published: (2023)
Text-Guided Multimodal Unified Industrial Anomaly Detection
by: Li, Zewen, et al.
Published: (2026)
by: Li, Zewen, et al.
Published: (2026)
Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillation
by: Sui, Wenbo, et al.
Published: (2024)
by: Sui, Wenbo, et al.
Published: (2024)
HGFormer: Topology-Aware Vision Transformer with HyperGraph Learning
by: Wang, Hao, et al.
Published: (2025)
by: Wang, Hao, et al.
Published: (2025)
Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detection
by: Costanzino, Alex, et al.
Published: (2026)
by: Costanzino, Alex, et al.
Published: (2026)
Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation
by: Jiang, Yuxin, et al.
Published: (2025)
by: Jiang, Yuxin, et al.
Published: (2025)
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection
by: Zhang, Yiheng, et al.
Published: (2024)
by: Zhang, Yiheng, et al.
Published: (2024)
Domain Adaptation-Based Crossmodal Knowledge Distillation for 3D Semantic Segmentation
by: Kang, Jialiang, et al.
Published: (2025)
by: Kang, Jialiang, et al.
Published: (2025)
Multimodal Industrial Anomaly Detection via Geometric Prior
by: Li, Min, et al.
Published: (2026)
by: Li, Min, et al.
Published: (2026)
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection
by: Li, Chunlei, et al.
Published: (2025)
by: Li, Chunlei, et al.
Published: (2025)
Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments
by: Yu, Jiawen, et al.
Published: (2025)
by: Yu, Jiawen, et al.
Published: (2025)
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
by: Li, Yuanze, et al.
Published: (2023)
by: Li, Yuanze, et al.
Published: (2023)
EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models
by: Peng, Xiaomeng, et al.
Published: (2026)
by: Peng, Xiaomeng, et al.
Published: (2026)
CFCML: A Coarse-to-Fine Crossmodal Learning Framework For Disease Diagnosis Using Multimodal Images and Tabular Data
by: Liu, Tianling, et al.
Published: (2026)
by: Liu, Tianling, et al.
Published: (2026)
Crossmodal learning for Crop Canopy Trait Estimation
by: Ayanlade, Timilehin T., et al.
Published: (2025)
by: Ayanlade, Timilehin T., et al.
Published: (2025)
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
by: Zhao, Shifang, et al.
Published: (2025)
by: Zhao, Shifang, et al.
Published: (2025)
Crossmodal Knowledge Distillation with WordNet-Relaxed Text Embeddings for Robust Image Classification
by: Guo, Chenqi, et al.
Published: (2025)
by: Guo, Chenqi, et al.
Published: (2025)
DNP-Guided Contrastive Reconstruction with a Reverse Distillation Transformer for Medical Anomaly Detection
by: Li, Luhu, et al.
Published: (2025)
by: Li, Luhu, et al.
Published: (2025)
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)
by: Tong, Xuan, et al.
Published: (2025)
Deep Industrial Image Anomaly Detection: A Survey
by: Liu, Jiaqi, et al.
Published: (2023)
by: Liu, Jiaqi, et al.
Published: (2023)
AFR-CLIP: Enhancing Zero-Shot Industrial Anomaly Detection with Stateless-to-Stateful Anomaly Feature Rectification
by: Yuan, Jingyi, et al.
Published: (2025)
by: Yuan, Jingyi, et al.
Published: (2025)
Wavelet-Enhanced PaDiM for Industrial Anomaly Detection
by: Gardner, Cory, et al.
Published: (2025)
by: Gardner, Cory, et al.
Published: (2025)
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection
by: Lin, Yuxuan, et al.
Published: (2024)
by: Lin, Yuxuan, et al.
Published: (2024)
IPAD: Industrial Process Anomaly Detection Dataset
by: Liu, Jinfan, et al.
Published: (2024)
by: Liu, Jinfan, et al.
Published: (2024)
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?
by: Chen, Zhiling, et al.
Published: (2025)
by: Chen, Zhiling, et al.
Published: (2025)
ONER: Online Experience Replay for Incremental Anomaly Detection
by: Jin, Yizhou, et al.
Published: (2024)
by: Jin, Yizhou, et al.
Published: (2024)
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection
by: Tu, Yuanpeng, et al.
Published: (2024)
by: Tu, Yuanpeng, et al.
Published: (2024)
EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO
by: Guan, Wei, et al.
Published: (2025)
by: Guan, Wei, et al.
Published: (2025)
ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining
by: Yao, Xincheng, et al.
Published: (2025)
by: Yao, Xincheng, et al.
Published: (2025)
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection
by: Chen, Xin, et al.
Published: (2024)
by: Chen, Xin, et al.
Published: (2024)
Collaborative Reconstruction and Repair for Multi-class Industrial Anomaly Detection
by: Wang, Qishan, et al.
Published: (2025)
by: Wang, Qishan, et al.
Published: (2025)
A Masked Reverse Knowledge Distillation Method Incorporating Global and Local Information for Image Anomaly Detection
by: Jiang, Yuxin, et al.
Published: (2025)
by: Jiang, Yuxin, et al.
Published: (2025)
ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
by: Chen, Qiuhui, et al.
Published: (2026)
by: Chen, Qiuhui, et al.
Published: (2026)
Towards High-Resolution Industrial Image Anomaly Detection
by: Zhang, Ximiao, et al.
Published: (2025)
by: Zhang, Ximiao, et al.
Published: (2025)
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising
by: Wang, Chengjie, et al.
Published: (2024)
by: Wang, Chengjie, et al.
Published: (2024)
MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
by: Jiang, Xi, et al.
Published: (2024)
by: Jiang, Xi, et al.
Published: (2024)
Similar Items
-
Unlocking the Potential of Reverse Distillation for Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2024) -
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2024) -
Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2025) -
Res$^2$CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment
by: Liu, Xinyue, et al.
Published: (2026) -
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
by: Costanzino, Alex, et al.
Published: (2023)