613 citations · 849 across the 26 of their papers we have counts for
5 papers · 1 filter
Observations on K-image Expansion of Image-Mixing Augmentation for Classification
Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo +3
Image-mixing augmentations (e.g., Mixup and CutMix), which typically involve mixing two images, have become the de-facto training techniques for image classification. Despite their…
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement
Beomyoung Kim, Youngjoon Yoo, Chaeeun Rhee +1
Weakly-supervised instance segmentation (WSIS) has been considered as a more challenging task than weakly-supervised semantic segmentation (WSSS). Compared to WSSS, WSIS requires i…
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations
Sungmin Cha, Naeun Ko, Youngjoon Yoo +1
We propose a novel and effective purification based adversarial defense method against pre-processor blind white- and black-box attacks. Our method is computationally efficient and…
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
Sungmin Cha, Beomyoung Kim, Youngjoon Yoo +1
This paper introduces a solid state-of-the-art baseline for a class-incremental semantic segmentation (CISS) problem. While the recent CISS algorithms utilize variants of the knowl…
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo +2
Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realisti…