activity
20212026
most citedActive Learning for Semantic Segmentation with Multi-class Label Query

1 citations · 1 across the 11 of their papers we have counts for

collaborators
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2026

ChimeraLoRA: Multi-Head LoRA-Guided Synthetic Datasets

Hoyoung Kim, Minwoo Jang, Jabin Koo +2

Beyond general recognition tasks, specialized domains and fine-grained settings often encounter data scarcity, especially for tail classes. To obtain less biased and more reliable…

cs.CV2024

Active Prompt Learning with Vision-Language Model Priors

Hoyoung Kim, Seokhee Jin, Changhwan Sung +2

Vision-language models (VLMs) have demonstrated remarkable zero-shot performance across various classification tasks. Nonetheless, their reliance on hand-crafted text prompts for e…

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…

cs.CV2023★ 1 cited

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…

cs.CV2023

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…