Cover Image

SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dai, Zhewei, Zeng, Shilei, Liu, Haotian, Li, Xurui, Xue, Feng, Zhou, Yu
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
by: Huang, Ziming, et al.
Published: (2024)

MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images
by: Li, Xurui, et al.
Published: (2024)

AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection
by: Huang, Dayou, et al.
Published: (2025)

MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples
by: Li, Xurui, et al.
Published: (2025)

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images
by: Li, Xurui, et al.
Published: (2025)

PointRFT: Explicit Reinforcement Fine-tuning for Point Cloud Few-shot Learning
by: Wang, Yankai, et al.
Published: (2026)

MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection
by: Zhang, Ximiao, et al.
Published: (2024)

Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection
by: Lu, Yilin, et al.
Published: (2025)

Few-shot Online Anomaly Detection and Segmentation
by: Wei, Shenxing, et al.
Published: (2024)

Stability Plasticity Decoupled Fine-tuning For Few-shot end-to-end Object Detection
by: Yin, Yuantao, et al.
Published: (2024)

The Devil is in the Few Shots: Iterative Visual Knowledge Completion for Few-shot Learning
by: Li, Yaohui, et al.
Published: (2024)

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection
by: Tian, Long, et al.
Published: (2025)

SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
by: Guo, Kaiyu, et al.
Published: (2025)

Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning
by: Zhao, Yaze, et al.
Published: (2026)

Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images
by: Brokman, Jonathan, et al.
Published: (2025)

Few-shot Image Generation via Masked Discrimination
by: Zhu, Jingyuan, et al.
Published: (2022)

Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners
by: Liu, Mushui, et al.
Published: (2024)

Search is All You Need for Few-shot Anomaly Detection
by: Wang, Qishan, et al.
Published: (2025)

MadCLIP: Few-shot Medical Anomaly Detection with CLIP
by: Shiri, Mahshid, et al.
Published: (2025)

Dual-path Frequency Discriminators for Few-shot Anomaly Detection
by: Bai, Yuhu, et al.
Published: (2024)

AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
by: Damm, Simon, et al.
Published: (2024)

AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
by: Hu, Teng, et al.
Published: (2023)

FLIER: Few-shot Language Image Models Embedded with Latent Representations
by: Zhou, Zhinuo, et al.
Published: (2024)

Few-shot Defect Image Generation based on Consistency Modeling
by: Shi, Qingfeng, et al.
Published: (2024)

Few-shot Calligraphy Style Learning
by: Chen, Fangda, et al.
Published: (2024)

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
by: He, Dexuan, et al.
Published: (2025)

Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)

Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
by: Chen, Shengzhuang, et al.
Published: (2024)

TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification
by: Qiao, Qian, et al.
Published: (2023)

FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model
by: Li, Yuanwei, et al.
Published: (2024)

Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
by: Liu, Jie, et al.
Published: (2024)

Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions
by: Li, Juncheng, et al.
Published: (2023)

Robust Fine-tuning of Zero-shot Models via Variance Reduction
by: Zhu, Beier, et al.
Published: (2024)

Few-shot Personalized Scanpath Prediction
by: Xue, Ruoyu, et al.
Published: (2025)

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

Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation
by: Lai, Bolin, et al.
Published: (2024)

Singular Value Fine-tuning for Few-Shot Class-Incremental Learning
by: Wang, Zhiwu, et al.
Published: (2025)

Few-shot Adaptation of Multi-modal Foundation Models: A Survey
by: Liu, Fan, et al.
Published: (2024)

CF-Font: Content Fusion for Few-shot Font Generation
by: Wang, Chi, et al.
Published: (2023)

MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
by: Jin, Xin, et al.
Published: (2024)