activity
20142024
most citedLLDiffusion: Learning Degradation Representations in Diffusion Models for Low-Light Image Enhancement

8 citations · 32 across the 19 of their papers we have counts for

collaborators

21 papers

cs.LG2024

Recent advances in interpretable machine learning using structure-based protein representations

Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev +5

Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure predict…

cs.CV2024

Hand-object reconstruction via interaction-aware graph attention mechanism

Taeyun Woo, Tae-Kyun Kim, Jinah Park

Estimating the poses of both a hand and an object has become an important area of research due to the growing need for advanced vision computing. The primary challenge involves und…

cs.CV2024

InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion

Jihyun Lee, Shunsuke Saito, Giljoo Nam +2

We present InterHandGen, a novel framework that learns the generative prior of two-hand interaction. Sampling from our model yields plausible and diverse two-hand shapes in close i…

cs.CV20241 cited

BiTT: Bi-directional Texture Reconstruction of Interacting Two Hands from a Single Image

Minje Kim, Tae-Kyun Kim

Creating personalized hand avatars is important to offer a realistic experience to users on AR / VR platforms. While most prior studies focused on reconstructing 3D hand shapes, so…

cs.CV20241 cited

Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder

Jinseok Kim, Tae-Kyun Kim

Super-resolution (SR) and image generation are important tasks in computer vision and are widely adopted in real-world applications. Most existing methods, however, generate images…

cs.CV2024

Energy-based Domain-Adaptive Segmentation with Depth Guidance

Jinjing Zhu, Zhedong Hu, Tae-Kyun Kim +1

Recent endeavors have been made to leverage self-supervised depth estimation as guidance in unsupervised domain adaptation (UDA) for semantic segmentation. Prior arts, however, ove…