1 citations · 3 across the 10 of their papers we have counts for
10 papers
VideoMaMa: Mask-Guided Video Matting via Generative Prior
Sangbeom Lim, Seoung Wug Oh, Jiahui Huang +3
Generalizing video matting models to real-world videos remains a significant challenge due to the scarcity of labeled data. To address this, we present Video Mask-to-Matte Model (V…
Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers
Chaehyun Kim, Heeseong Shin, Eunbeen Hong +5
Text-to-image diffusion models excel at translating language prompts into photorealistic images by implicitly grounding textual concepts through their cross-modal attention mechani…
InterRVOS: Interaction-aware Referring Video Object Segmentation
Woojeong Jin, Seongchan Kim, Jaeho Lee +1
Referring video object segmentation (RVOS) aims to segment objects in a video described by a natural language expression. However, most existing approaches focus on segmenting only…
URECA: Unique Region Caption Anything
Sangbeom Lim, Junwan Kim, Heeji Yoon +2
Region-level captioning aims to generate natural language descriptions for specific image regions while highlighting their distinguishing features. However, existing methods strugg…
S^4M: Boosting Semi-Supervised Instance Segmentation with SAM
Heeji Yoon, Heeseong Shin, Eunbeen Hong +4
Semi-supervised instance segmentation poses challenges due to limited labeled data, causing difficulties in accurately localizing distinct object instances. Current teacher-student…
Multi-Granularity Video Object Segmentation
Sangbeom Lim, Seongchan Kim, Seungjun An +3
Current benchmarks for video segmentation are limited to annotating only salient objects (i.e., foreground instances). Despite their impressive architectural designs, previous work…
Referring Video Object Segmentation via Language-aligned Track Selection
Seongchan Kim, Woojeong Jin, Sangbeom Lim +3
Referring video object segmentation (RVOS) requires tracking and segmenting an object throughout a video according to a given natural language expression, demanding both complex mo…
GaussianTalker: Real-Time High-Fidelity Talking Head Synthesis with Audio-Driven 3D Gaussian Splatting
Kyusun Cho, Joungbin Lee, Heeji Yoon +4
We propose GaussianTalker, a novel framework for real-time generation of pose-controllable talking heads. It leverages the fast rendering capabilities of 3D Gaussian Splatting (3DG…
Talk3D: High-Fidelity Talking Portrait Synthesis via Personalized 3D Generative Prior
Jaehoon Ko, Kyusun Cho, Joungbin Lee +4
Recent methods for audio-driven talking head synthesis often optimize neural radiance fields (NeRF) on a monocular talking portrait video, leveraging its capability to render high-…
Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection
Kwangrok Ryoo, Yeonsik Jo, Seungjun Lee +5
For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations…