activity
20222024
most citedTask Discrepancy Maximization for Fine-grained Few-Shot Classification

8 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Progressive Proxy Anchor Propagation for Unsupervised Semantic Segmentation

Hyun Seok Seong, WonJun Moon, SuBeen Lee +1

The labor-intensive labeling for semantic segmentation has spurred the emergence of Unsupervised Semantic Segmentation. Recent studies utilize patch-wise contrastive learning based…

cs.CV2024

Mitigating Background Shift in Class-Incremental Semantic Segmentation

Gilhan Park, WonJun Moon, SuBeen Lee +2

Class-Incremental Semantic Segmentation(CISS) aims to learn new classes without forgetting the old ones, using only the labels of the new classes. To achieve this, two popular stra…

cs.CV20231 cited

Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification

SuBeen Lee, WonJun Moon, Hyun Seok Seong +1

The difficulty of the fine-grained image classification mainly comes from a shared overall appearance across classes. Thus, recognizing discriminative details, such as eyes and bea…

cs.CV20235 cited

Leveraging Hidden Positives for Unsupervised Semantic Segmentation

Hyun Seok Seong, WonJun Moon, SuBeen Lee +1

Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer…

cs.CV20228 cited

Task Discrepancy Maximization for Fine-grained Few-Shot Classification

SuBeen Lee, WonJun Moon, Jae-Pil Heo

Recognizing discriminative details such as eyes and beaks is important for distinguishing fine-grained classes since they have similar overall appearances. In this regard, we intro…