5 citations · 9 across the 9 of their papers we have counts for
16 papers
Elevating Flow-Guided Video Inpainting with Reference Generation
Suhwan Cho, Seoung Wug Oh, Sangyoun Lee +1
Video inpainting (VI) is a challenging task that requires effective propagation of observable content across frames while simultaneously generating new content not present in the o…
Transforming Static Images Using Generative Models for Video Salient Object Detection
Suhwan Cho, Minhyeok Lee, Jungho Lee +1
In many video processing tasks, leveraging large-scale image datasets is a common strategy, as image data is more abundant and facilitates comprehensive knowledge transfer. A typic…
WWW: Where, Which and Whatever Enhancing Interpretability in Multimodal Deepfake Detection
Juho Jung, Sangyoun Lee, Jooeon Kang +1
All current benchmarks for multimodal deepfake detection manipulate entire frames using various generation techniques, resulting in oversaturated detection accuracies exceeding 94%…
Enhancing Temporal Action Localization: Advanced S6 Modeling with Recurrent Mechanism
Sangyoun Lee, Juho Jung, Changdae Oh +1
Temporal Action Localization (TAL) is a critical task in video analysis, identifying precise start and end times of actions. Existing methods like CNNs, RNNs, GCNs, and Transformer…
Improving Unsupervised Video Object Segmentation via Fake Flow Generation
Suhwan Cho, Minhyeok Lee, Jungho Lee +4
Unsupervised video object segmentation (VOS), also known as video salient object detection, aims to detect the most prominent object in a video at the pixel level. Recently, two-st…
ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic Fusion
Sungmin Woo, Wonjoon Lee, Woo Jin Kim +2
Self-supervised multi-frame monocular depth estimation relies on the geometric consistency between successive frames under the assumption of a static scene. However, the presence o…