3 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
Extreme Point Supervised Instance Segmentation
Hyeonjun Lee, Sehyun Hwang, Suha Kwak
This paper introduces a novel approach to learning instance segmentation using extreme points, i.e., the topmost, leftmost, bottommost, and rightmost points, of each object. These…
Active Label Correction for Semantic Segmentation with Foundation Models
Hoyoung Kim, Sehyun Hwang, Suha Kwak +1
Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive. Although useful priors such as foundat…
Active Learning for Semantic Segmentation with Multi-class Label Query
Sehyun Hwang, Sohyun Lee, Hoyoung Kim +3
This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions…
Adaptive Superpixel for Active Learning in Semantic Segmentation
Hoyoung Kim, Minhyeon Oh, Sehyun Hwang +2
Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learn…
Learning to Detect Semantic Boundaries with Image-level Class Labels
Namyup Kim, Sehyun Hwang, Suha Kwak
This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object cla…