12 citations · 18 across the 8 of their papers we have counts for
8 papers
S^4M: Boosting Semi-Supervised Instance Segmentation with SAM
Heeji Yoon, Heeseong Shin, Eunbeen Hong +4
Semi-supervised instance segmentation poses challenges due to limited labeled data, causing difficulties in accurately localizing distinct object instances. Current teacher-student…
SPACE: SPAtial-aware Consistency rEgularization for anomaly detection in Industrial applications
Daehwan Kim, Hyungmin Kim, Daun Jeong +2
In this paper, we propose SPACE, a novel anomaly detection methodology that integrates a Feature Encoder (FE) into the structure of the Student-Teacher method. The proposed method…
ContextMix: A context-aware data augmentation method for industrial visual inspection systems
Hyungmin Kim, Donghun Kim, Pyunghwan Ahn +3
While deep neural networks have achieved remarkable performance, data augmentation has emerged as a crucial strategy to mitigate overfitting and enhance network performance. These…
Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery
Hyungmin Kim, Sungho Suh, Daehwan Kim +3
Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learni…
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels
Daehwan Kim, Kwangrok Ryoo, Hansang Cho +1
Annotating the dataset with high-quality labels is crucial for performance of deep network, but in real world scenarios, the labels are often contaminated by noise. To address this…
AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation
Hyungmin Kim, Sungho Suh, Sunghyun Baek +4
We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which r…
Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels
Jiwon Kim, Kwangrok Ryoo, Junyoung Seo +4
Establishing dense correspondences across semantically similar images remains a challenging task due to the significant intra-class variations and background clutters. Traditionall…
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee +5
Semi-supervised learning (SSL) has recently proven to be an effective paradigm for leveraging a huge amount of unlabeled data while mitigating the reliance on large labeled data. C…