12 citations · 29 across the 8 of their papers we have counts for
9 papers · 1 filter
Learning from Adversity: Semantic-Aware Mask Refinement through Adversarial Perturbation
Beomyoung Kim, Sung Ju Hwang
Despite significant advances in image segmentation, even state-of-the-art models produce masks with imperfect boundaries, semantic inconsistencies, and structural errors. Mask refi…
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…
Rethinking Saliency-Guided Weakly-Supervised Semantic Segmentation
Beomyoung Kim, Donghyun Kim, Sung Ju Hwang
This paper presents a fresh perspective on the role of saliency maps in weakly-supervised semantic segmentation (WSSS) and offers new insights and research directions based on our…
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…
The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation
Beomyoung Kim, Joonhyun Jeong, Dongyoon Han +1
In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance…