activity
20212024
most citedA Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective

7 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Toward Interactive Regional Understanding in Vision-Large Language Models

Jungbeom Lee, Sanghyuk Chun, Sangdoo Yun

Recent Vision-Language Pre-training (VLP) models have demonstrated significant advancements. Nevertheless, these models heavily rely on image-text pairs that capture only coarse an…

cs.CV2023

Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild

Gyeongsik Moon, Hongsuk Choi, Sanghyuk Chun +2

Recovering 3D human mesh in the wild is greatly challenging as in-the-wild (ITW) datasets provide only 2D pose ground truths (GTs). Recently, 3D pseudo-GTs have been widely used to…

cs.LG20235 cited

Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization

Sangwon Jung, Taeeon Park, Sanghyuk Chun +1

Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approxim…

cs.LG20227 cited

A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective

Chanwoo Park, Sangdoo Yun, Sanghyuk Chun

We propose the first unified theoretical analysis of mixed sample data augmentation (MSDA), such as Mixup and CutMix. Our theoretical results show that regardless of the choice of…

cs.CV2021

Few-shot Font Generation with Weakly Supervised Localized Representations

Song Park, Sanghyuk Chun, Junbum Cha +2

Automatic few-shot font generation aims to solve a well-defined, real-world problem because manual font designs are expensive and sensitive to the expertise of designers. Existing…