7 citations · 10 across the 6 of their papers we have counts for
6 papers
Confidence-Based Feature Imputation for Graphs with Partially Known Features
Daeho Um, Jiwoong Park, Seulki Park +1
This paper investigates a missing feature imputation problem for graph learning tasks. Several methods have previously addressed learning tasks on graphs with missing features. How…
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models
Seulki Park, Daeho Um, Hajung Yoon +3
With the extensive use of vision-language models in various downstream tasks, evaluating their robustness is crucial. In this paper, we propose a benchmark for assessing the robust…
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
Jihwan Bang, Hyunseo Koh, Seulki Park +3
Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. A large body of continual learning (CL) methods, h…
The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification
Seulki Park, Youngkyu Hong, Byeongho Heo +2
The problem of class imbalanced data is that the generalization performance of the classifier deteriorates due to the lack of data from minority classes. In this paper, we propose…
Influence-Balanced Loss for Imbalanced Visual Classification
Seulki Park, Jongin Lim, Younghan Jeon +1
In this paper, we propose a balancing training method to address problems in imbalanced data learning. To this end, we derive a new loss used in the balancing training phase that a…
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels
Seulki Park, Hwanjun Song, Daeho Um +3
Deep neural network can easily overfit to even noisy labels due to its high capacity, which degrades the generalization performance of a model. To overcome this issue, we propose a…