Domain Adaptation of Attention Heads for Zero-shot Anomaly Detection
Fuente:
arXiv
Guardado en:
| Autores principales: | Jeong, Kiyoon, Heo, Jaehyuk, Son, Junyeong, Kang, Pilsung |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutions
por: Heo, Jaehyuk, et al.
Publicado: (2025)
por: Heo, Jaehyuk, et al.
Publicado: (2025)
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model
por: Heo, Jaehyuk, et al.
Publicado: (2024)
por: Heo, Jaehyuk, et al.
Publicado: (2024)
Technical Report of NICE Challenge at CVPR 2024: Caption Re-ranking Evaluation Using Ensembled CLIP and Consensus Scores
por: Jeong, Kiyoon, et al.
Publicado: (2024)
por: Jeong, Kiyoon, et al.
Publicado: (2024)
Unified Language-driven Zero-shot Domain Adaptation
por: Yang, Senqiao, et al.
Publicado: (2024)
por: Yang, Senqiao, et al.
Publicado: (2024)
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection
por: Kim, Donghyeong, et al.
Publicado: (2025)
por: Kim, Donghyeong, et al.
Publicado: (2025)
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
por: Zhou, Qihang, et al.
Publicado: (2023)
por: Zhou, Qihang, et al.
Publicado: (2023)
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain
por: Huang, Hong, et al.
Publicado: (2025)
por: Huang, Hong, et al.
Publicado: (2025)
MoECLIP: Patch-Specialized Experts for Zero-shot Anomaly Detection
por: Park, Jun Yeong, et al.
Publicado: (2026)
por: Park, Jun Yeong, et al.
Publicado: (2026)
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection
por: Zhu, Jiawen, et al.
Publicado: (2024)
por: Zhu, Jiawen, et al.
Publicado: (2024)
Zero-Shot Head Swapping in Real-World Scenarios
por: Kang, Taewoong, et al.
Publicado: (2025)
por: Kang, Taewoong, et al.
Publicado: (2025)
Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection
por: Lee, Hyogun, et al.
Publicado: (2025)
por: Lee, Hyogun, et al.
Publicado: (2025)
TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection
por: Zhou, Qihang, et al.
Publicado: (2025)
por: Zhou, Qihang, et al.
Publicado: (2025)
TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection
por: Salehi, Alireza, et al.
Publicado: (2026)
por: Salehi, Alireza, et al.
Publicado: (2026)
AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation
por: Fang, Qingqing, et al.
Publicado: (2025)
por: Fang, Qingqing, et al.
Publicado: (2025)
Attention Head Purification: A New Perspective to Harness CLIP for Domain Generalization
por: Wang, Yingfan, et al.
Publicado: (2024)
por: Wang, Yingfan, et al.
Publicado: (2024)
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
por: Zhu, Jiaqi, et al.
Publicado: (2024)
por: Zhu, Jiaqi, et al.
Publicado: (2024)
Towards Zero-shot 3D Anomaly Localization
por: Wang, Yizhou, et al.
Publicado: (2024)
por: Wang, Yizhou, et al.
Publicado: (2024)
PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detection
por: Zhou, Qihang, et al.
Publicado: (2025)
por: Zhou, Qihang, et al.
Publicado: (2025)
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
por: Ma, Wenxin, et al.
Publicado: (2025)
por: Ma, Wenxin, et al.
Publicado: (2025)
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection
por: Bai, Yuhu, et al.
Publicado: (2025)
por: Bai, Yuhu, et al.
Publicado: (2025)
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework
por: Cheng, Yuqi, et al.
Publicado: (2024)
por: Cheng, Yuqi, et al.
Publicado: (2024)
GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection
por: Ham, Jiyul, et al.
Publicado: (2024)
por: Ham, Jiyul, et al.
Publicado: (2024)
Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention
por: Wu, Wensheng, et al.
Publicado: (2026)
por: Wu, Wensheng, et al.
Publicado: (2026)
Task-Specific Zero-shot Quantization-Aware Training for Object Detection
por: Li, Changhao, et al.
Publicado: (2025)
por: Li, Changhao, et al.
Publicado: (2025)
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection
por: Chen, Xuhai, et al.
Publicado: (2023)
por: Chen, Xuhai, et al.
Publicado: (2023)
Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation
por: Du, Zhipeng, et al.
Publicado: (2023)
por: Du, Zhipeng, et al.
Publicado: (2023)
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection
por: Zhou, Qihang, et al.
Publicado: (2024)
por: Zhou, Qihang, et al.
Publicado: (2024)
Few-shot Online Anomaly Detection and Segmentation
por: Wei, Shenxing, et al.
Publicado: (2024)
por: Wei, Shenxing, et al.
Publicado: (2024)
SIDA: Synthetic Image Driven Zero-shot Domain Adaptation
por: Kim, Ye-Chan, et al.
Publicado: (2025)
por: Kim, Ye-Chan, et al.
Publicado: (2025)
TexAvatars : Hybrid Texel-3D Representations for Stable Rigging of Photorealistic Gaussian Head Avatars
por: Lee, Jaeseong, et al.
Publicado: (2025)
por: Lee, Jaeseong, et al.
Publicado: (2025)
Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation
por: Seo, Minseok, et al.
Publicado: (2026)
por: Seo, Minseok, et al.
Publicado: (2026)
Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection
por: Salehi, Alireza, et al.
Publicado: (2025)
por: Salehi, Alireza, et al.
Publicado: (2025)
Quantifying Context Bias in Domain Adaptation for Object Detection
por: Son, Hojun, et al.
Publicado: (2024)
por: Son, Hojun, et al.
Publicado: (2024)
One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation
por: Yu, Zhixuan, et al.
Publicado: (2024)
por: Yu, Zhixuan, et al.
Publicado: (2024)
Infrared Domain Adaptation with Zero-Shot Quantization
por: Sevsay, Burak, et al.
Publicado: (2024)
por: Sevsay, Burak, et al.
Publicado: (2024)
GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning
por: Deng, Zehao, et al.
Publicado: (2026)
por: Deng, Zehao, et al.
Publicado: (2026)
HeadHunt-VAD: Hunting Robust Anomaly-Sensitive Heads in MLLM for Tuning-Free Video Anomaly Detection
por: Cai, Zhaolin, et al.
Publicado: (2025)
por: Cai, Zhaolin, et al.
Publicado: (2025)
Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images
por: Brokman, Jonathan, et al.
Publicado: (2025)
por: Brokman, Jonathan, et al.
Publicado: (2025)
Zero-shot Quantization: A Comprehensive Survey
por: Kim, Minjun, et al.
Publicado: (2025)
por: Kim, Minjun, et al.
Publicado: (2025)
Mitigating Context Bias in Domain Adaptation for Object Detection using Mask Pooling
por: Son, Hojun, et al.
Publicado: (2025)
por: Son, Hojun, et al.
Publicado: (2025)
Ejemplares similares
-
Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutions
por: Heo, Jaehyuk, et al.
Publicado: (2025) -
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model
por: Heo, Jaehyuk, et al.
Publicado: (2024) -
Technical Report of NICE Challenge at CVPR 2024: Caption Re-ranking Evaluation Using Ensembled CLIP and Consensus Scores
por: Jeong, Kiyoon, et al.
Publicado: (2024) -
Unified Language-driven Zero-shot Domain Adaptation
por: Yang, Senqiao, et al.
Publicado: (2024) -
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection
por: Kim, Donghyeong, et al.
Publicado: (2025)