58 citations · 83 across the 10 of their papers we have counts for
11 papers
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.…
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…
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…
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation
Kukjin Choi, Jihun Yi, Jisoo Mok +1
Continuous efforts are being made to advance anomaly detection in various manufacturing processes to increase the productivity and safety of industrial sites. Deep learning replace…
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…
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Saehyung Lee, Changhwa Park, Hyungyu Lee +3
Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have c…
Interpretation of NLP models through input marginalization
Siwon Kim, Jihun Yi, Eunji Kim +1
To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring…
Information-Theoretic Visual Explanation for Black-Box Classifiers
Jihun Yi, Eunji Kim, Siwon Kim +1
In this work, we attempt to explain the prediction of any black-box classifier from an information-theoretic perspective. For each input feature, we compare the classifier outputs…
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…
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…
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning
Jaeyoon Yoo, Heonseok Ha, Jihun Yi +5
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-…