24 citations · 28 across the 7 of their papers we have counts for
7 papers
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…
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…
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…