Papers with Code 아카이브

3D Anomaly Detection

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하위 태스크: 2
평가표: 3
보존된 평가 기록: 34

3D-only Anomaly Detection. Structures out of normal distribution are detected from the 3D-only point cloud.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/aseuteurideu/STEAL

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Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection

https://github.com/benedictstar/Joint-VAD

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Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection

https://github.com/boschresearch/LatentOE-AD

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Latent Outlier Exposure for Anomaly Detection with Contaminated Data

https://github.com/caoyunkang/gpt4v-for-generic-anomaly-detection

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Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

https://github.com/carolchenyx/mgfn

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MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection

https://github.com/chopper-233/anomaly-shapenet

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Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network

https://github.com/codnjsqkr/FastAno_official

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FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation

https://github.com/cool-xuan/bn-wvad

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BatchNorm-based Weakly Supervised Video Anomaly Detection

https://github.com/demetrislappas/ddl

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Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

https://github.com/eavnjeong/ief-vad

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Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly Detection

https://github.com/eliahuhorwitz/3D-ADS

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Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

https://github.com/erktkdg/MTFL

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MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos

https://github.com/fdjingliu/nsvad

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Networking Systems for Video Anomaly Detection: A Tutorial and Survey

https://github.com/fjchange/MIST_VAD

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MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection

https://github.com/fudanyliu/gvaed

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Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

https://github.com/gdwang08/jigsaw-vad

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Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles

https://github.com/guijiejie/dcmd-main

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Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detection

https://github.com/hazardfy/fiba

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FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis

https://github.com/henrryzh1/MA-PDM

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Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model

https://github.com/henrryzh1/UR-DMU

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Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection

https://github.com/heyonghu/tiny-ppg

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Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices

https://github.com/huchao-ai/apn

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Normal Learning in Videos with Attention Prototype Network

https://github.com/hustCYQ/MiniShift-Simple3D

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Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects

https://github.com/hzzzzzhappy/mc4ad

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Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection

https://github.com/implabunipr/movad

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Memory-augmented Online Video Anomaly Detection

https://github.com/inkiinki/mi-uap

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Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial Perturbations

https://github.com/jLooo/VADMamba

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VADMamba: Exploring State Space Models for Fast Video Anomaly Detection

https://github.com/jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection

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MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection

https://github.com/jarartur/histopathologyaugmentationresearch

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Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation

https://github.com/joos2010kj/clip-tsa

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CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection

https://github.com/jqtangust/hawk

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Hawk: Learning to Understand Open-World Video Anomalies

https://github.com/jx-zhong-for-academic-purpose/GCN-Anomaly-Detection

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Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection

https://github.com/kapildeshpande/anomaly-detection-in-surveillance-videos

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Anomaly detection in surveillance videos using transformer based attention model

https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-

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Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate

https://github.com/khalooei/ALOCC-CVPR2018

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Adversarially Learned One-Class Classifier for Novelty Detection

https://github.com/ktr-hubrt/MPN

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Learning Normal Dynamics in Videos with Meta Prototype Network

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