On the public citation graph

A SAM-guided Two-stream Lightweight Model for Anomaly Detection

0 signed · 76 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-031-19803-8_5openalex-snapshot

TinyViT: Fast Pretraining Distillation for Small Vision Transformers

inferredcites →https://doi.org/10.1007/978-3-031-19821-2_27openalex-snapshot

Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization

inferredcites →https://doi.org/10.1007/978-3-031-20056-4_23openalex-snapshot

SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation

inferredcites →https://doi.org/10.1007/s11263-022-01578-9openalex-snapshot

Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization

inferredcites →https://doi.org/10.1007/s11704-021-1237-4openalex-snapshot

Meta-BN Net for few-shot learning

inferredcites →https://doi.org/10.1007/s11704-022-1349-5openalex-snapshot

Representation learning via an integrated autoencoder for unsupervised domain adaptation

inferredcites →https://doi.org/10.1007/s11704-022-2134-1openalex-snapshot

Combating with extremely noisy samples in weakly supervised slot filling for automatic diagnosis

inferredcites →https://doi.org/10.1007/s13042-024-02355-5openalex-snapshot

Self-refined variational transformer for image-conditioned layout generation

inferredcites →https://doi.org/10.1016/j.engappai.2024.107886openalex-snapshot

CSPNeXt: A new efficient token hybrid backbone

inferredcites →https://doi.org/10.1016/j.patcog.2020.107706openalex-snapshot

Reconstruction by inpainting for visual anomaly detection

inferredcites →https://doi.org/10.1109/access.2022.3193699openalex-snapshot

CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization

inferredcites →https://doi.org/10.1109/cvpr.2018.00716openalex-snapshot

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

inferredcites →https://doi.org/10.1109/cvpr.2019.00982openalex-snapshot

MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00424openalex-snapshot

Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00867openalex-snapshot

Towards Visually Explaining Variational Autoencoders

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00954openalex-snapshot

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

inferredcites →https://doi.org/10.1109/cvpr52688.2022.00951openalex-snapshot

Anomaly Detection via Reverse Distillation from One-Class Embedding

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01165openalex-snapshot

Decoupled Knowledge Distillation

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01392openalex-snapshot

Towards Total Recall in Industrial Anomaly Detection

inferredcites →https://doi.org/10.1109/cvpr52729.2023.00381openalex-snapshot

DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection

inferredcites →https://doi.org/10.1109/cvpr52729.2023.01075openalex-snapshot

ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation

inferredcites →https://doi.org/10.1109/cvpr52729.2023.01562openalex-snapshot

Prototypical Residual Networks for Anomaly Detection and Localization

inferredcites →https://doi.org/10.1109/cvpr52729.2023.01954openalex-snapshot

SimpleNet: A Simple Network for Image Anomaly Detection and Localization

inferredcites →https://doi.org/10.1109/cvpr52729.2023.02346openalex-snapshot

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

inferredcites →https://doi.org/10.1109/cvpr52729.2023.02348openalex-snapshot

Revisiting Reverse Distillation for Anomaly Detection

inferredcites →https://doi.org/10.1109/iccv.2019.00179openalex-snapshot

Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

inferredcites →https://doi.org/10.1109/iccv.2019.00339openalex-snapshot

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

inferredcites →https://doi.org/10.1109/iccv48922.2021.00822openalex-snapshot

DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection

inferredcites →https://doi.org/10.1109/iccv48922.2021.00951openalex-snapshot

Emerging Properties in Self-Supervised Vision Transformers

inferredcites →https://doi.org/10.1109/iccv51070.2023.00110openalex-snapshot

SegGPT: Towards Segmenting Everything In Context

inferredcites →https://doi.org/10.1109/iccv51070.2023.00371openalex-snapshot

Segment Anything

inferredcites →https://doi.org/10.1109/iccv51070.2023.00626openalex-snapshot

Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection

inferredcites →https://doi.org/10.1109/iccv51070.2023.01549openalex-snapshot

Rethinking Vision Transformers for MobileNet Size and Speed

inferredcites →https://doi.org/10.1109/tcsvt.2023.3344099openalex-snapshot

DCFP: Distribution Calibrated Filter Pruning for Lightweight and Accurate Long-Tail Semantic Segmentation

inferredcites →https://doi.org/10.1109/tim.2022.3196436openalex-snapshot

Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey

inferredcites →https://doi.org/10.1109/wacv51458.2022.00188openalex-snapshot

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

inferredcites →https://doi.org/10.1136/bmj.309.6947.102openalex-snapshot

Statistics Notes: Diagnostic tests 2: predictive values

inferredcites →https://doi.org/10.1145/3097983.3098052openalex-snapshot

Anomaly Detection with Robust Deep Autoencoders

inferredcites →https://doi.org/10.1145/3131342openalex-snapshot

An Efficient Motion Detection and Tracking Scheme for Encrypted Surveillance Videos

inferredcites →https://doi.org/10.1145/3284750openalex-snapshot

CM-GANs

inferredcites →https://doi.org/10.1145/3343031.3350998openalex-snapshot

Exploring Background-bias for Anomaly Detection in Surveillance Videos

inferredcites →https://doi.org/10.1145/3394171.3413813openalex-snapshot

Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches

inferredcites →https://doi.org/10.1145/3431768openalex-snapshot

3D Tensor Auto-encoder with Application to Video Compression

inferredcites →https://doi.org/10.1145/3464423openalex-snapshot

Deep Learning for Medical Anomaly Detection – A Survey

inferredcites →https://doi.org/10.1145/3574131.3574463openalex-snapshot

Robust Anomaly Detection and Localization via Simulated Anomalies

inferredcites →https://doi.org/10.1145/3614435openalex-snapshot

Diverse Image Captioning via Conditional Variational Autoencoder and Dual Contrastive Learning

inferredcites →https://doi.org/10.1162/dint_a_00144openalex-snapshot

Ensemble Making Few-Shot Learning Stronger

inferredcites →https://doi.org/10.1162/dint_a_00146openalex-snapshot

Faster Zero-shot Multi-modal Entity Linking via Visual-Linguistic Representation

inferredcites →https://doi.org/10.1162/dint_a_00232openalex-snapshot

Evaluation on ChatGPT for Chinese Language Understanding

inferredcites →https://doi.org/10.1609/aaai.v35i10.17054openalex-snapshot

Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision

inferredcites →https://doi.org/10.2139/ssrn.4370101openalex-snapshot

Cross-Cbam: A Lightweight Network for Scene Segmentation

inferredcites →https://doi.org/10.3156/jsoft.29.5_177_2openalex-snapshot

GAN（Generative Adversarial Nets）

inferredcites →https://doi.org/10.48550/arxiv.2304.07193openalex-snapshot

DINOv2: Learning Robust Visual Features without Supervision

inferredcites →https://doi.org/10.5220/0007364503720380openalex-snapshot

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

inferredcites →https://doi.org/10.5281/zenodo.8086135openalex-snapshot

Weakly Supervised Learning for Industrial Optical Inspection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-10185-3_4openalex-snapshot

KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices

inferred← citeshttps://doi.org/10.1007/s10462-025-11287-7openalex-snapshot

A survey of deep learning for industrial visual anomaly detection

inferred← citeshttps://doi.org/10.1007/s11042-026-21809-2openalex-snapshot

A comprehensive survey on industrial image anomaly detection

inferred← citeshttps://doi.org/10.1007/s44163-026-02102-5openalex-snapshot

Hospital abnormal target and personnel behavior detection using improved YOLOv8

inferred← citeshttps://doi.org/10.1016/j.aei.2025.103593openalex-snapshot

Leveraging large-scale vision foundation models for automated construction and demolition waste recognition

inferred← citeshttps://doi.org/10.1016/j.asoc.2026.114650openalex-snapshot

Boundary-guided large-scale vision model for unified multi-domain industrial anomaly detection

inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103139openalex-snapshot

A survey on RGB, 3D, and multimodal approaches for unsupervised industrial image anomaly detection

inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103517openalex-snapshot

Foundation models and Transformers for anomaly detection: A survey

inferred← citeshttps://doi.org/10.1016/j.knosys.2026.115515openalex-snapshot

SAM-IAD: Injecting specific knowledge into SAM for industrial anomaly detection

inferred← citeshttps://doi.org/10.1088/1361-6501/add6c8openalex-snapshot

From data analysis to intelligent maintenance: a survey on visual defect detection in aero-engines

inferred← citeshttps://doi.org/10.1109/access.2025.3580864openalex-snapshot

Self-Supervised Image Anomaly Detection Through Diverse Pseudo Anomaly Insertion

inferred← citeshttps://doi.org/10.1109/ecai69016.2026.11613678openalex-snapshot

Attention-Enhanced U-Net for Robust N95 FFR Segmentation in Infrared Imagery

inferred← citeshttps://doi.org/10.1109/tce.2025.3596250openalex-snapshot

AutoKAN: A Federated Lightweight Anomaly Detection Framework for Securing Constrained IoT Healthcare Diabetes Monitoring Systems

inferred← citeshttps://doi.org/10.1109/tim.2025.3588927openalex-snapshot

LScAD: A Large–Small Model Collaboration Framework for Unsupervised Industrial Anomaly Detection

inferred← citeshttps://doi.org/10.1109/tmm.2025.3590934openalex-snapshot

SF-City: A Source-Free Domain Adaptation Method for City-Scale Point Cloud Semantic Segmentation

inferred← citeshttps://doi.org/10.1111/exsy.70447openalex-snapshot

SAM ‐ CompAug : Segment Anything‐Assisted Synthetic Data Augmentation for Industrial Part Detection

inferred← citeshttps://doi.org/10.1139/cgj-2024-0736openalex-snapshot

Enhancement and assessment of large vision models for 3D particle reconstruction from X-ray tomography

inferred← citeshttps://doi.org/10.1145/3797034openalex-snapshot

A New Semi-Supervised Video Anomaly Detection Baseline in Lack of Anomalous Samples

inferred← citeshttps://doi.org/10.1145/3816703openalex-snapshot

Enhancing Object Detection with Active Exploration and Spatiotemporal Aggregation

inferred← citeshttps://doi.org/10.1145/3828722openalex-snapshot

STPrompt \(\boldsymbol{++}\) : Prompting Vision-Language Models for Weakly Supervised Video Anomaly Detection and Fine-Grained Localization

inferred← citeshttps://doi.org/10.14801/jkiit.2025.23.8.1openalex-snapshot

Zero-shot Model-based Abnormal Behavior Detection Fusion Model in Unmanned Store

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.