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cs.CV2024
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…
cs.CV2024
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…