Registration Based Few-Shot Anomaly Detection

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Accurate Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers

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Memory guided dual discrepancy network with domain adaptation for few-shot anomaly detection

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DecAD: Decoupling Anomalies in Latent Space for Multi-Class Unsupervised Anomaly Detection

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Sea-CLIP: Mining Semantic-Aware Representations for Few-Shot Anomaly Detection with CLIP

Guo, X., Chen, Z., Castillo, C. D., Wang, H., & Liu, X. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00360 · Cited by 3

PatchCLIP: Enhencing CLIP with Pair-Images for Zero-Shot Anomaly Detection

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Small sample pipeline DR defect detection based on smooth variational autoencoder and enhanced detection head faster RCNN

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Efficient Defect Detection of Rotating Goods under the Background of Intelligent Retail

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Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding

Nivarthi, C. P., & Sick, B. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00218 · Cited by 4

Prioritized Local Matching Network for Cross-Category Few-Shot Anomaly Detection

Deng, H., Luo, H., Zhai, W., Guo, Y., Cao, Y. H., & Kang, Y. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3385743 · Cited by 5

Scale Adaptive Attention Network for Accurate Defect Detection From Metal Parts

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LoGics: Anomaly detection of key components of high-speed rail via zero-shot learning

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Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection

Yao, H., Luo, W., Cao, Y., Zhang, Y., Yu, W., & Shen, W. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3595539 · Cited by 4

Deep support vector data description based on correntropy for few-shot anomaly detection

Shang, Q., Yang, J., Fu, F., & Ma, J. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105074 · Cited by 2

A Contrastive Learning-Guided Confident Meta-Learning for Zero Shot Anomaly Detection

Aqeel, M., Skočaj, D., Cristani, M., & Setti, F. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00156 · Cited by 3

FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization

Gu, Z., Zhu, B., Zhu, G., Chen, Y., Tang, M., & Wang, J. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3671805 · Cited by 10

Few-shot fine-grained recognition in remote sensing ship images with global and local feature aggregation

Zhou, G., Huang, L., & Zhang, X. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.06.077 · Cited by 2

GLASNet: A Global–Local Anomaly Generator and Salient Channel Feature Selector-Based Network for Accurate Few-Shot Industrial Anomaly Detection

Luo, A., Wen, G., Chen, Z., & Liu, X. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3578508 · Cited by 2

Nearest Memory Augmented Feature Reconstruction for Unified Anomaly Detection

Wu, H., Wang, C., Jian, Z., Lai, Y., Song, L., & Yang, F. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8148-9_28 · Cited by 2

Dual-Path Frequency Discriminators for Few-Shot Anomaly DetectionPreprint

Bai, Y., Zhang, J., Dong, Y., Cao, Y., & Tian, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4862099 · Cited by 3

TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks

Lee, H.-W., & Lai, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00396 · Cited by 1

Patch-Wise Neighborhood Feature Distribution Modeling for Anomaly Detection in Locomotive Roof Inspections

Fu, Z., Pan, X., & Zhang, G. (2024) · IEEE Access · doi:10.1109/access.2024.3476469 · Cited by 2

Towards Zero-shot 3D Anomaly Localization

Wang, Y., Peng, K., & Fu, Y. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00148 · Cited by 4

SPACE: SPAtial-Aware Consistency rEgularization for Anomaly Detection in Industrial Applications

Kim, D.-H., Kim, H., Jeong, D., Suh, S., & Cho, H. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00698 · Cited by 1

Single-Layer Distillation with Fourier Convolutions for Texture Anomaly Detection

Thomine, S., & Snoussi, H. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00870 · Cited by 1

DFTGL: Domain Filtered and Target Guided Learning for few-shot anomaly detection

Zhang, J., Song, Y., & Yang, Z. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104457 · Cited by 1

PADiff: Reconstruction From Patch to Pixel With Normality-Guided Diffusion Model for Unsupervised Anomaly Localization

Zuo, Z., Dong, J., Wu, Y., Qu, Y., & Wu, Z. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3572438 · Cited by 1

MAS-ZSAS: A Zero-Shot Anomaly Segmentation Framework with Multi-attribute Guided Text Prompts

Yuan, X., Liang, G., Hua, L., Xie, S., & Gan, W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9856-1_36 · Cited by 1

Few-shot anomaly detection and classification through reinforced data selection with a combinatorial reward

Han, X., Xu, D., Yuan, S., & Wu, X. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02572-6 · Cited by 1

MetaCAN: Improving Generalizability of Few-shot Anomaly Detection with Meta-learning

Lv, Z., Zhang, J., Jian, S., Huang, C., Zhang, H., Pang, G., & Liu, Z. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761253 · Cited by 2

Few-shot Anomaly Detection Based on Diffusion Generation Model and Multi-scale Feature Manipulation

Zhao, S., Liu, A., Qu, D., & Zhao, S. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227359 · Cited by 1

MADPOT: Medical Anomaly Detection with CLIP Adaptation and Partial Optimal Transport

Shiri, M., Beyan, C., & Murino, V. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-10185-3_20 · Cited by 1

A Two-Stage Detection Method for Tiny Defects in Transparent Components Based on Pseudolabel Generation

Jiang, Z., Shi, D., Pan, D., Yu, H., & Wang, Y. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3651494 · Cited by 1

Graph Interaction Prompt Network for Few-Shot Medical Image Anomaly Detection

Zhang, Z., Tao, F., Xiang, T., Zhao, F., & Xie, G.-S. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356618 · Cited by 1

Prior-Guided Feature Sampling and Restoration for Few-Shot Industrial Anomaly Detection

Hu, X., & Yang, H. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3670014 · Cited by 1

CRAD-HOPE: brain-inspired nested learning framework for few-shot anomaly detection

Cao, W., Cao, J., & Wu, W. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00577-x · Cited by 1

High-frequency semantic hierarchical dynamic feature fusion network for chip surface defect detection

Zhang, F., Pan, B., Wang, Y., Zhou, C., Zhao, Y., & Duan, J. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae527b · Cited by 1

SimpleZ3D: A simple framework for zero-shot 3D industrial anomaly detection

Gao, H., Zhao, W., Zhong, Y., Wang, M., & Zhang, J. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114237 · Cited by 1

A review of binocular vision stereo matching algorithms

Niu, Y., Gao, L., Yu, W., Huang, Z., & Liu, H. (2026) · Journal of the European Optical Society Rapid Publications · doi:10.1051/jeos/2026053 · Cited by 1

Industrial Anomaly Detection using Vision Based Cross Transformers

Naqi, M. M., & Tahir, M. A. (2023) · International Conference on IT and Industrial Technologies (ICIT) · doi:10.1109/icit59216.2023.10335864 · Cited by 1

Fast Adaptive Coarse-to-Fine PatchMatch-Based Defect Detection on Nonflat Prints

Shi, C., Jia, B., Zhang, C., Zang, X., Zhang, J., Xin, J., & Zhu, C. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3343769 · Cited by 1

Registration Based Feature Core-Set Model for Defect Detection

Jing, X. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10450867 · Cited by 1

FGSNet: A Finer-Grained Siamese Network for Industrial Few-Shot Anomaly Detection

Han, D., Xu, L., Zhou, M., Li, G., & Li, M. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831932 · Cited by 1

A Hierarchical Flow for Few-shot Anomaly Detection via Global-local Aggregation Strategy

Wan, Y., Fan, T., & Yao, L. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889988 · Cited by 1

On The Relationship Between Visual Anomaly-Free and Anomalous Representations

Sadrani, R., Bachan, A., & Sharma, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_16 · Cited by 1

The Current State, Advantages, and Recommendations for the Development of the Large Model Industry in Shanghai

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Adapsyn: Anomaly Detection Based on Triplet Training with Adaptive Anomaly Synthesis (Chinamm2024)Preprint

Zhou, S., Lin, C., Chen, Z., Guo, B., & Zhao, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4889011

Real-Time Industrial Anomaly Detection via Sparse Reconstruction

Zhou, C., Wan, J., & Zhao, X. (2024) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar61438.2024.10670980

LMATDet:Lightweight Multi-scale Aerial TargetDetection with attention and efficient lossPreprint

Zhao, Y. (2024) · Research Square · doi:10.21203/rs.3.rs-5335228/v1

IRAD: Input-Reference Joint Driven Reconstruction for Unified Anomaly Detection

Chen, Z., Yao, X., Luo, Y., Liu, Z., Zhang, B., & Zhang, C. (2024) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip63160.2024.10849884

Registration and Convolutional Multidimensional Attention Module for Few-Shot Anomaly Detection

Xie, X., Xiong, S., Zheng, W., & Cai, T. (2025) · The European Journal on Artificial Intelligence · doi:10.1177/30504554241297616

Enforced clustering for zero-to-one-shot texture anomaly detection

Varnousefaderani, B. A., Rakhmonov, A. A. U., Kim, J.-S., & Kim, J. (2025) · Machine Vision and Applications · doi:10.1007/s00138-025-01670-3

Patch Feature Transformation: An Anomaly Detection Method with Succinct Feature Filtering

Guo, Y., Xu, G., Yin, J., & Wang, S. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425590062

Few-Shot Industrial Anomaly Detection via Proxy Task Learning on Normal Samples

He, S., Xu, R., Huang, W., Chen, Y., & Wang, L. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00220 · Cited by 1

Memory-Guided Hierarchical Feature Reconstruction for Multi-class Unsupervised Anomaly Detection

Huang, K., Zhou, S., Hu, W., Gao, Y., Pan, F., & Jiang, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4276-2_16 · Cited by 1

InfraredStereo3D: Breaking Night Vision Limits with Perspective Projection Positional Encoding and Groundbreaking Infrared Dataset

Niu, Y., Liu, L., Huang, F., Ma, J., Zheng, C., Jiang, Y., An, T., Zhao, Z., & Chen, S. (2025) · Remote Sensing · doi:10.3390/rs17122035

A Diffusion-Based Framework with Feature Retrieval for Multi-Class Industrial Anomaly Detection

Chen, J., Zhang, R., & Zhong, G. (2025) · International Conference on Information Science, Electrical and Automation Engineering (ISEAE) · doi:10.1109/iseae64934.2025.11041745

NLPA-AD: normal label propagation algorithm for zero-shot texture anomaly detection

Zhang, J., Song, Y., Yang, Z., & Wang, C. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06733-6

Unsupervised Defect Detection Based on Fast Retrieval of Reconfiguration Feature

Huang, J., Xiong, Z., & Cao, J. (2025) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice64954.2025.11064481

Logical Anomaly Detection with Text-based Logic via Component-Aware Contrastive Language-Image Training

Lee, S.-E., Kim, S., An, S., Lee, S.-C., & Park, S. H. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737032 · Cited by 1

CLIP-DSA: A CLIP-Based Discriminative and Self-supervised Framework for Few-Shot Anomaly Detection

Zeng, S., Chen, Y., Li, M. N., Wu, Y., & Tian, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9891-2_3

A Hierarchical Patch Feature Distribution Network for Industrial Multiscale Defect Detection

Zheng, Z., Yang, H. S., & Yin, Z. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3598017 · Cited by 1

FastRecon++: Enhancing Few-Shot Anomaly Detection for Electric Vehicle Manufacturing

Tavakoli, N., Verma, R., Gilvari, A., Kar, N. C., & Kobti, Z. (2025) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie62713.2025.11124813

AAAD: Adaptive Activated Anomaly Detection on Varied Backgrounds

Miyamoto, K., Lee, T., & Minezawa, A. (2025) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss65446.2025.11149930

AnomalyNLP: Noisy-Label Prompt Learning for Few-Shot Industrial Anomaly Detection

Li, H., & Qian, J. (2025) · Electronics · doi:10.3390/electronics14204016 · Cited by 2

Learning Invariant Discriminative Patterns for Unified Anomaly Detection

Xing, C., Xu, Y., Xu, Y., & Cui, L. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755179

ReCLIP: Reconstruction-Refined Zero-/Few-Shot Anomaly Classification and Segmentation

Zhang, L., Shi, Y., Lan, S., Gao, F., Qin, H., & Wang, N. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209291

A defect detection method based on feature reconstruction using diffusion models

Chen, X.-F., Fu, F., & Fang, M. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-05037-z

Efficient Odd-One-Out Anomaly Detection

Chito, S., Rabino, P., & Tommasi, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-10185-3_31 · Cited by 1

$$\text {AD}^2$$: Anomaly Detection During Training an Distillation-Based Anomaly Detection Model

Chen, K., Wang, X., Yang, H., Chen, H., Wang, Y., Zhao, S., & Ding, G. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5761-5_1

A framework for unsupervised anomaly detection in real-world cable inspection

Lu, T., & Shao, P. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-025-05087-3

Semantic-Guided and Enhanced Memory Few-Shot Display Anomaly Detection

Sijie, L., Chen, H., & Zhixi, W. (2025) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip68645.2025.11352655

CLIP-Core: A Few-Sample Anomaly Detection Method for Surface Defects

Liang, X., Rao, J., & Lin, S. (2026) · IEEE Access · doi:10.1109/access.2026.3660103

Self-Supervised CLIP-Guided for Few-Shot Industrial Anomaly Detection

Chen, Y., Xu, Y. W., Wang, T., Zhai, Y., Tan, K., Zhou, J., Coscia, P., Genovese, A., & Chen, C. L. P. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3661696

SEM-CLIP 2.0: Precise Zero-/Few-Shot Learning for Nanoscale Defect Detection in SEM Image

Jin, Q., Li, R., Jiang, Y., Liu, Y., Lu, X., Chen, Y., Gao, D., Sun, Q., & Zhuo, C. (2026) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2026.3663889

Self-Attention Enhanced Student–Teacher Framework for Visual Anomaly Detection in Industrial Images

Amjad, H. A. R., Shallari, I., O’Nils, M., & Qureshi, F. Z. (2026) · IEEE Access · doi:10.1109/access.2026.3670056

Semi-supervised deep learning for robust anomaly detection in smart manufacturing systems

Le, T. (2024) · theses.fr (ABES) · doi:10.70675/19d23228zf030z4df7za3abz596d36577818

LeafCLIP: A detection method for the lesion areas of outdoor plants in complex lighting scenarios

Dong, S., Wei, B., Chen, Q., & Zhang, Y. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111770

TunCLIP: Adaptive Multi-Scale Zero-Shot Anomaly Detection via Vision-Language Feature Infiltration and Semantic Enhancement

Zhang, T., Pang, W., & Lu, X. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3684998

Fine-Tuning CLIP Models with Convolutional Features for Medical Anomaly Detection

Fu, J., & Xue, M. (2025) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait67930.2025.11522646

ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learning

Yao, X., Shi, C., Zhao, M., Zhai, G., & Zhang, C. L. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02885-1 · Cited by 2

Enhancing medical anomaly detection via text-adapted few-shot learning with visual-language models

Mao, K., Hou, S., Fang, H., Zhao, J., & Xiao, X. (2026) · The Visual Computer · doi:10.1007/s00371-026-04498-8

RGPKD: Reconstruction-Guided and Prompt-Enhanced Asymmetric Knowledge Distillation for Anomaly Detection

Wang, K., Qin, C., Chi, J., Chen, C., & Yu, T. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3688668

Information Retrieval-Based Zero-Shot Surface Defect Semantic Segmentation

Bai, H., Zhang, Y., Shan, D., & Wu, Z. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3691093

SAM-LLaVA: A segmentation-aware vision-language framework for industrial defect diagnosis

An, S., Wang, C., & Dong, X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114242

Anatomic-Decoupling Adaptation: A Unified Framework for Zero/Few-Shot Medical Anomaly Detection

Lin, Z., Wang, F., Cai, W., & Cai, H. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810835

CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection

Cai, J., Chen, J., Zeng, D., Pang, J., Liang, Q., Zhong, X., & Wu, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37016-7_23

GPS: GlobalCLIP-PatchCore-SAM Based Zero-Shot Anomaly Detection and Localization in Smart Manufacturing

Park, J. W., & Jeong, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30488-9_28

FECF-CLIP: Fourier-based CLIP framework for zero-shot industrial anomaly detection

Wang, Z., Lin, J., Pang, F., Zeng, P., & Yang, A. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582023

Vision expert guided inspection for industrial anomaly detection

Zhu, X., Cui, W., Tao, Y., & Wang, X. (2026) · PLoS ONE · doi:10.1371/journal.pone.0353291

Generative Few-Shot Siamese Networks for Anomaly Detection: Application to Pipeline Leakage in Nuclear Power Plants

Jeong, J.-H., Choi, Y.-R., Choi, Y., Cho, D.-Y., & Kim, M.-S. (2026) · Sensors · doi:10.3390/s26144372

Deployment-Oriented Memory-Based Visual Inspection of Injection-Molded Parts

Raza, M. M., Ali, B. B., Qureshi, M. S., Rizvi, O., Anees, M., Khalid, A., Wasay, M. A., Hassan, W., & Ahsan, U. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-26217-2_16

Alignment-Aware 3D Point Cloud Anomaly Detection with Adversarial Normalizing Flows

Jiménez-García, A., Garcia, J. A., Arias, H. F. G., Gil-González, J., & Peña, D. A. C. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8070206

Industrial Visual Anomaly Detection in Robotics: Methods, Datasets, and Deployable System Architectures

Tran, T.-H., & Le, X.-B. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2888-1_9

Zero-Shot Anomaly Detection via Learnable Complementary Prompts and Cross-Modal Interaction

Wang, G., & Wang, Z. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426510122

Continual Subspace Refinement for One-Shot Industrial Anomaly DetectionPreprint

Leng, S. H., Urmonov, O., Shah, R. A., & Kim, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7180179

PADFormer: Pose-Agnostic Anomaly Detection from Sparse View Images

Wang, R., Qian, Y., Yu, F., Lu, Y., Wang, D., Zhang, H., & Huang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37602-2_11

GRPO-Anomaly: Reinforcement Fine-Tuning Vision--Language Model for Industrial Defects Detection and Reasoning

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