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20172024
most citedPatch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation

58 citations · 76 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV20241 cited

CKNN: Cleansed k-Nearest Neighbor for Unsupervised Video Anomaly Detection

Jihun Yi, Sungroh Yoon

In this paper, we address the problem of unsupervised video anomaly detection (UVAD). The task aims to detect abnormal events in test video using unlabeled videos as training data.…

cs.CV2024

Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models

Jihun Yi, Dahuin Jung, Sungroh Yoon

The task of image anomaly detection (IAD) aims to identify deviations from normality in image data. These anomalies are patterns that deviate significantly from what the IAD model…

cs.CV2024

Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach

Saehyung Lee, Sangwon Yu, Junsung Park +2

In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the gen…

cs.CV20212 cited

BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation

Jungbeom Lee, Jihun Yi, Chaehun Shin +1

Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on…

cs.CV2020

iCaps: An Interpretable Classifier via Disentangled Capsule Networks

Dahuin Jung, Jonghyun Lee, Jihun Yi +1

We propose an interpretable Capsule Network, iCaps, for image classification. A capsule is a group of neurons nested inside each layer, and the one in the last layer is called a cl…

cs.CV202058 cited

Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation

Jihun Yi, Sungroh Yoon

In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anom…