1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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…
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation
Hyunsu Rhee, Dongchan Min, Sunil Hwang +2
Real-time video segmentation is a crucial task for many real-world applications such as autonomous driving and robot control. Since state-of-the-art semantic segmentation models ar…