17 citations · 25 across the 4 of their papers we have counts for
4 papers · 1 filter
ZIM: Zero-Shot Image Matting for Anything
Beomyoung Kim, Chanyong Shin, Joonhyun Jeong +5
The recent segmentation foundation model, Segment Anything Model (SAM), exhibits strong zero-shot segmentation capabilities, but it falls short in generating fine-grained precise m…
Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Matting
Beomyoung Kim, Myeong Yeon Yi, Joonsang Yu +2
This paper presents a new practical training method for human matting, which demands delicate pixel-level human region identification and significantly laborious annotations. To re…
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning
Beomyoung Kim, Joonsang Yu, Sung Ju Hwang
Panoptic segmentation, combining semantic and instance segmentation, stands as a cutting-edge computer vision task. Despite recent progress with deep learning models, the dynamic n…
Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding
Sunoh Kim, Jungchan Cho, Joonsang Yu +2
In the weakly supervised temporal video grounding study, previous methods use predetermined single Gaussian proposals which lack the ability to express diverse events described by…