49 citations · 87 across the 14 of their papers we have counts for
16 papers
Dual Recursive Feedback on Generation and Appearance Latents for Pose-Robust Text-to-Image Diffusion
Jiwon Kim, Pureum Kim, SeonHwa Kim +3
Recent advancements in controllable text-to-image (T2I) diffusion models, such as Ctrl-X and FreeControl, have demonstrated robust spatial and appearance control without requiring…
Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions
Jiwon Kim, Soohyun Hwang, Dong-O Kim +3
The first algorithm, called Oneta, for a novel task of multi-style image enhancement is proposed in this work. Oneta uses two point operators sequentially: intensity enhancement wi…
Identity-preserving Distillation Sampling by Fixed-Point Iterator
SeonHwa Kim, Jiwon Kim, Soobin Park +5
Score distillation sampling (SDS) demonstrates a powerful capability for text-conditioned 2D image and 3D object generation by distilling the knowledge from learned score functions…
D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection
Dinh Phat Do, Taehoon Kim, Jaemin Na +4
Domain adaptation for object detection typically entails transferring knowledge from one visible domain to another visible domain. However, there are limited studies on adapting fr…
PANDAS: Prototype-based Novel Class Discovery and Detection
Tyler L. Hayes, César R. de Souza, Namil Kim +3
Object detectors are typically trained once and for all on a fixed set of classes. However, this closed-world assumption is unrealistic in practice, as new classes will inevitably…
Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Images
Jiwon Kim, Byeongho Heo, Sangdoo Yun +2
Semantic correspondence methods have advanced to obtaining high-quality correspondences employing complicated networks, aiming to maximize the model capacity. However, despite the…
HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalization
Daeun Lee, Minhyeok Heo, Jiwon Kim
Lane detection is a vital task for vehicles to navigate and localize their position on the road. To ensure reliable driving, lane detection models must have robust generalization p…
Associative Partial Domain Adaptation
Youngeun Kim, Sungeun Hong, Seunghan Yang +3
Partial Adaptation (PDA) addresses a practical scenario in which the target domain contains only a subset of classes in the source domain. While PDA should take into account both c…
Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization
Yunho Jeon, Yongseok Choi, Jaesun Park +3
Training a deep neural network with a small amount of data is a challenging problem as it is vulnerable to overfitting. However, one of the practical difficulties that we often fac…
Restore from Restored: Single Image Denoising with Pseudo Clean Image
Seunghwan Lee, Dongkyu Lee, Donghyeon Cho +2
In this study, we propose a simple and effective fine-tuning algorithm called "restore-from-restored", which can greatly enhance the performance of fully pre-trained image denoisin…
Restore from Restored: Video Restoration with Pseudo Clean Video
Seunghwan Lee, Donghyeon Cho, Jiwon Kim +1
In this study, we propose a self-supervised video denoising method called "restore-from-restored." This method fine-tunes a pre-trained network by using a pseudo clean video during…
Self-Supervised Fast Adaptation for Denoising via Meta-Learning
Seunghwan Lee, Donghyeon Cho, Jiwon Kim +1
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and thes…
Fast Adaptation to Super-Resolution Networks via Meta-Learning
Seobin Park, Jinsu Yoo, Donghyeon Cho +2
Conventional supervised super-resolution (SR) approaches are trained with massive external SR datasets but fail to exploit desirable properties of the given test image. On the othe…
Key Instance Selection for Unsupervised Video Object Segmentation
Donghyeon Cho, Sungeun Hong, Sungil Kang +1
This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames seque…
Discriminative Few-Shot Learning Based on Directional Statistics
Junyoung Park, Subin Yi, Yongseok Choi +2
Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy. We propose a novel algorit…
Deeply-Recursive Convolutional Network for Image Super-Resolution
Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions). Increasin…