3 citations · 4 across the 2 of their papers we have counts for
7 papers
Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
Nayeong Kim, Juwon Kang, Sungsoo Ahn +2
We study the problem of training an unbiased and accurate model given a dataset with multiple biases. This problem is challenging since the multiple biases cause multiple undesirab…
CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment
Hyeongmin Lee, Kyoungkook Kang, Jungseul Ok +1
Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment. However, these approaches…
MedBN: Robust Test-Time Adaptation against Malicious Test Samples
Hyejin Park, Jeongyeon Hwang, Sunung Mun +2
Test-time adaptation (TTA) has emerged as a promising solution to address performance decay due to unforeseen distribution shifts between training and test data. While recent TTA m…
Addressing Feature Imbalance in Sound Source Separation
Jaechang Kim, Jeongyeon Hwang, Soheun Yi +2
Neural networks often suffer from a feature preference problem, where they tend to overly rely on specific features to solve a task while disregarding other features, even if those…
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…
Combating Label Distribution Shift for Active Domain Adaptation
Sehyun Hwang, Sohyun Lee, Sungyeon Kim +2
We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent a…