8 citations · 21 across the 17 of their papers we have counts for
18 papers
BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing
Chaewon Park, Soyoon Lee, Naeun Lee +3
Real image editing enables precise manipulation of visual content, yet existing methods often fail in complex multi-object scenarios, causing semantic blending, object duplication,…
CMTM: Cross-Modal Token Modulation for Unsupervised Video Object Segmentation
Inseok Jeon, Suhwan Cho, Minhyeok Lee +6
Recent advances in unsupervised video object segmentation have highlighted the potential of two-stream architectures that integrate appearance and motion cues. However, fully lever…
Cross Pseudo Labeling For Weakly Supervised Video Anomaly Detection
Dayeon Lee, Donghyeong Kim, Chaewon Park +2
Weakly supervised video anomaly detection aims to detect anomalies and identify abnormal categories with only video-level labels. We propose CPL-VAD, a dual-branch framework with c…
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection
Donghyeong Kim, Chaewon Park, Suhwan Cho +4
Zero-shot anomaly detection (ZSAD) aims to identify anomalies in unseen categories by leveraging CLIP's zero-shot capabilities to match text prompts with visual features. A key cha…
Guided Slot Attention for Unsupervised Video Object Segmentation
Minhyeok Lee, Suhwan Cho, Dogyoon Lee +3
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects…
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection
Chaewon Park, Minhyeok Lee, Suhwan Cho +2
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets. These approaches work by learning to mod…
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
Suhwan Cho, Minhyeok Lee, Seunghoon Lee +3
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods lever…
Boundary-aware Camouflaged Object Detection via Deformable Point Sampling
Minhyeok Lee, Suhwan Cho, Chaewon Park +3
The camouflaged object detection (COD) task aims to identify and segment objects that blend into the background due to their similar color or texture. Despite the inherent difficul…
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection
Donghyeong Kim, Chaewon Park, Suhwan Cho +1
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some method…
Unsupervised Video Object Segmentation via Prototype Memory Network
Minhyeok Lee, Suhwan Cho, Seunghoon Lee +2
Unsupervised video object segmentation aims to segment a target object in the video without a ground truth mask in the initial frame. This challenging task requires extracting feat…
Pixel-Level Equalized Matching for Video Object Segmentation
Suhwan Cho, Woo Jin Kim, MyeongAh Cho +4
Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation. If surjecti…
Tackling Background Distraction in Video Object Segmentation
Suhwan Cho, Heansung Lee, Minhyeok Lee +4
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos. One of the main challenges in this task is the existence of background d…
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
Minhyeok Lee, Chaewon Park, Suhwan Cho +1
RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in de…
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection
Chaewon Park, Minhyeok Lee, MyeongAh Cho +1
Recent anomaly detection algorithms have shown powerful performance by adopting frame predicting autoencoders. However, these methods face two challenging circumstances. First, the…
Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss
Chaewon Park, Minhyeok Lee, MyeongAh Cho +1
UNet-based methods have shown outstanding performance in salient object detection (SOD), but are problematic in two aspects. 1) Indiscriminately integrating the encoder feature, wh…
EdgeConv with Attention Module for Monocular Depth Estimation
Minhyeok Lee, Sangwon Hwang, Chaewon Park +1
Monocular depth estimation is an especially important task in robotics and autonomous driving, where 3D structural information is essential. However, extreme lighting conditions an…
FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation
Chaewon Park, MyeongAh Cho, Minhyeok Lee +1
Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos. Especially, the prediction based approa…
LFI-CAM: Learning Feature Importance for Better Visual Explanation
Kwang Hee Lee, Chaewon Park, Junghyun Oh +1
Class Activation Mapping (CAM) is a powerful technique used to understand the decision making of Convolutional Neural Network (CNN) in computer vision. Recently, there have been at…