8 citations · 41 across the 34 of their papers we have counts for
5 papers · 1 filter
DepthFlow: Exploiting Depth-Flow Structural Correlations for Unsupervised Video Object Segmentation
Suhwan Cho, Minhyeok Lee, Jungho Lee +2
Unsupervised video object segmentation (VOS) aims to detect the most prominent object in a video. Recently, two-stream approaches that leverage both RGB images and optical flow hav…
TransFlow: Motion Knowledge Transfer from Video Diffusion Models to Video Salient Object Detection
Suhwan Cho, Minhyeok Lee, Jungho Lee +2
Video salient object detection (SOD) relies on motion cues to distinguish salient objects from backgrounds, but training such models is limited by scarce video datasets compared to…
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…
CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images
Jungho Lee, Donghyeong Kim, Dogyoon Lee +6
3D Gaussian Splatting (3DGS) has gained significant attention due to its high-quality novel view rendering, motivating research to address real-world challenges. A critical issue i…
Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object Segmentation
Suhwan Cho, Seunghoon Lee, Minhyeok Lee +2
Referring video object segmentation aims to segment and track a target object in a video using a natural language prompt. Existing methods typically fuse visual and textual feature…