Imagen de Portada

Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Rolih, Blaž, Ameln, Dick, Vaidya, Ashwin, Akcay, Samet
Formato:	Preprint
Publicado:	2024
Materias:	Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance
por: Bertoldo, Joao P. C., et al.
Publicado: (2024)

From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
por: Heckler-Kram, Lars, et al.
Publicado: (2025)

Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection
por: Baitieva, Aimira, et al.
Publicado: (2025)

SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection
por: Rolih, Blaž, et al.
Publicado: (2024)

No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes
por: Rolih, Blaž, et al.
Publicado: (2025)

Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation
por: Wolf, Filip, et al.
Publicado: (2026)

ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing
por: Rolih, Blaž, et al.
Publicado: (2026)

Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices
por: Rolih, Blaž, et al.
Publicado: (2025)

Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations
por: Rolih, Blaž, et al.
Publicado: (2026)

Towards High-Resolution Industrial Image Anomaly Detection
por: Zhang, Ximiao, et al.
Publicado: (2025)

Divide, Ensemble and Conquer: The Last Mile on Unsupervised Domain Adaptation for Semantic Segmentation
por: Lian, Tao, et al.
Publicado: (2024)

DC-Net: Divide-and-Conquer for Salient Object Detection
por: Zhu, Jiayi, et al.
Publicado: (2023)

DCA: Dividing and Conquering Amnesia in Incremental Object Detection
por: Zhang, Aoting, et al.
Publicado: (2025)

Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery
por: Tu, Yuanpeng, et al.
Publicado: (2024)

Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformer
por: Osman, Nada, et al.
Publicado: (2024)

Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection
por: Kang, Xiaolu, et al.
Publicado: (2026)

Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models
por: Wang, Wenbin, et al.
Publicado: (2024)

ShadowHack: Hacking Shadows via Luminance-Color Divide and Conquer
por: Hu, Jin, et al.
Publicado: (2024)

Divide-and-Conquer Approach to Holistic Cognition in High-Similarity Contexts with Limited Data
por: Wang, Shijie, et al.
Publicado: (2026)

AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation
por: Miao, Junwen, et al.
Publicado: (2025)

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection
por: Tang, Hao, et al.
Publicado: (2024)

Divide and Conquer: Decoupled Representation Alignment for Multimodal World Models
por: Xiao, Junyuan, et al.
Publicado: (2026)

DCText: Scheduled Attention Masking for Visual Text Generation via Divide-and-Conquer Strategy
por: Song, Jaewoo, et al.
Publicado: (2025)

MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation
por: Wang, Yanhui, et al.
Publicado: (2023)

DCDM: Divide-and-Conquer Diffusion Models for Consistency-Preserving Video Generation
por: Zhao, Haoyu, et al.
Publicado: (2026)

Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation
por: Cong, Runmin, et al.
Publicado: (2025)

Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
por: Wang, Chong, et al.
Publicado: (2024)

ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining
por: Yao, Xincheng, et al.
Publicado: (2025)

DAC: 2D-3D Retrieval with Noisy Labels via Divide-and-Conquer Alignment and Correction
por: Gan, Chaofan, et al.
Publicado: (2024)

DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation
por: Kim, Jihun, et al.
Publicado: (2025)

DCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation
por: Ouyang, Junyi, et al.
Publicado: (2026)

Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D Gaussian Spatting
por: Yan, Zhiying, et al.
Publicado: (2025)

PDC-Net: Pattern Divide-and-Conquer Network for Pelvic Radiation Injury Segmentation
por: Xiong, Xinyu, et al.
Publicado: (2025)

Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
por: Li, Hanxi, et al.
Publicado: (2024)

Divide and Conquer: Object Co-occurrence Helps Mitigate Simplicity Bias in OOD Detection
por: Dai, Boyang, et al.
Publicado: (2026)

FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection
por: Isaac-Medina, Brian K. S., et al.
Publicado: (2024)

Multimodal Industrial Anomaly Detection via Geometric Prior
por: Li, Min, et al.
Publicado: (2026)

VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion
por: Hicsonmez, Samet, et al.
Publicado: (2025)

Divide and Conquer: Grounding a Bleeding Areas in Gastrointestinal Image with Two-Stage Model
por: Lin, Yu-Fan, et al.
Publicado: (2024)

DivCon: Divide and Conquer for Complex Numerical and Spatial Reasoning in Text-to-Image Generation
por: Jia, Yuhao, et al.
Publicado: (2024)