most citedPUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising

7 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach

Saehyung Lee, Sangwon Yu, Junsung Park +2

In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the gen…

cs.CV20241 cited

Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather

Junsung Park, Kyungmin Kim, Hyunjung Shim

Existing LiDAR semantic segmentation methods often struggle with performance declines in adverse weather conditions. Previous work has addressed this issue by simulating adverse we…

cs.CV2024

Precision matters: Precision-aware ensemble for weakly supervised semantic segmentation

Junsung Park, Hyunjung Shim

Weakly Supervised Semantic Segmentation (WSSS) employs weak supervision, such as image-level labels, to train the segmentation model. Despite the impressive achievement in recent W…

cs.LG20241 cited

DAFA: Distance-Aware Fair Adversarial Training

Hyungyu Lee, Saehyung Lee, Hyemi Jang +3

The disparity in accuracy between classes in standard training is amplified during adversarial training, a phenomenon termed the robust fairness problem. Existing methodologies aim…

eess.IV20237 cited

PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising

Hyemi Jang, Junsung Park, Dahuin Jung +3

Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synth…