activity
20192023
most citedResults of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

313 citations · 376 across the 17 of their papers we have counts for

collaborators

17 papers

cs.SI2023★ 17 cited

Classification of Edge-dependent Labels of Nodes in Hypergraphs

Minyoung Choe, Sunwoo Kim, Jaemin Yoo +1

A hypergraph is a data structure composed of nodes and hyperedges, where each hyperedge is an any-sized subset of nodes. Due to the flexibility in hyperedge size, hypergraphs repre…

cs.CV2023★ 1 cited

User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques

Sunwoo Kim, Wooseok Jang, Hyunsu Kim +4

Recent text-driven image editing in diffusion models has shown remarkable success. However, the existing methods assume that the user's description sufficiently grounds the context…

cs.SI2023★ 12 cited

How Transitive Are Real-World Group Interactions? -- Measurement and Reproduction

Sunwoo Kim, Fanchen Bu, Minyoung Choe +2

Many real-world interactions (e.g., researcher collaborations and email communication) occur among multiple entities. These group interactions are naturally modeled as hypergraphs.…

cs.CV2023★ 2 cited

Diffusion Model for Dense Matching

Jisu Nam, Gyuseong Lee, Sunwoo Kim +4

The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term. While conventional techniques focused on defining han…

cs.CV2023

Semantic-Preserving Augmentation for Robust Image-Text Retrieval

Sunwoo Kim, Kyuhong Shim, Luong Trung Nguyen +1

Image text retrieval is a task to search for the proper textual descriptions of the visual world and vice versa. One challenge of this task is the vulnerability to input image and…

cs.CV2022★ 2 cited

Controllable Style Transfer via Test-time Training of Implicit Neural Representation

Sunwoo Kim, Youngjo Min, Younghun Jung +1

We propose a controllable style transfer framework based on Implicit Neural Representation that pixel-wisely controls the stylized output via test-time training. Unlike traditional…