48 citations · 126 across the 32 of their papers we have counts for
35 papers
Auto-regressive transformation for image alignment
Kanggeon Lee, Soochahn Lee, Kyoung Mu Lee
Existing methods for image alignment struggle in cases involving feature-sparse regions, extreme scale and field-of-view differences, and large deformations, often resulting in sub…
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution
Reyhaneh Neshatavar, Mohsen Yavartanoo, Sanghyun Son +1
Single image super-resolution (SISR) is a challenging ill-posed problem that aims to up-sample a given low-resolution (LR) image to a high-resolution (HR) counterpart. Due to the d…
Human Part-wise 3D Motion Context Learning for Sign Language Recognition
Taeryung Lee, Yeonguk Oh, Kyoung Mu Lee
In this paper, we propose P3D, the human part-wise motion context learning framework for sign language recognition. Our main contributions lie in two dimensions: learning the part-…
Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction
Hyeongjin Nam, Daniel Sungho Jung, Yeonguk Oh +1
Despite recent advances in 3D human mesh reconstruction, domain gap between training and test data is still a major challenge. Several prior works tackle the domain gap problem via…
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks
Cheeun Hong, Kyoung Mu Lee
Although quantization has emerged as a promising approach to reducing computational complexity across various high-level vision tasks, it inevitably leads to accuracy loss in image…
ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion
Sangmin Hong, Mohsen Yavartanoo, Reyhaneh Neshatavar +1
Point cloud completion addresses filling in the missing parts of a partial point cloud obtained from depth sensors and generating a complete point cloud. Although there has been st…
Recovering 3D Hand Mesh Sequence from a Single Blurry Image: A New Dataset and Temporal Unfolding
Yeonguk Oh, JoonKyu Park, Jaeha Kim +2
Hands, one of the most dynamic parts of our body, suffer from blur due to their active movements. However, previous 3D hand mesh recovery methods have mainly focused on sharp hand…
Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation
Hongsuk Choi, Hyeongjin Nam, Taeryung Lee +2
Recently, a few self-supervised representation learning (SSL) methods have outperformed the ImageNet classification pre-training for vision tasks such as object detection. However,…
MEIL-NeRF: Memory-Efficient Incremental Learning of Neural Radiance Fields
Jaeyoung Chung, Kanggeon Lee, Sungyong Baik +1
Hinged on the representation power of neural networks, neural radiance fields (NeRF) have recently emerged as one of the promising and widely applicable methods for 3D object and s…
MultiAct: Long-Term 3D Human Motion Generation from Multiple Action Labels
Taeryung Lee, Gyeongsik Moon, Kyoung Mu Lee
We tackle the problem of generating long-term 3D human motion from multiple action labels. Two main previous approaches, such as action- and motion-conditioned methods, have limita…
MonoNHR: Monocular Neural Human Renderer
Hongsuk Choi, Gyeongsik Moon, Matthieu Armando +3
Existing neural human rendering methods struggle with a single image input due to the lack of information in invisible areas and the depth ambiguity of pixels in visible areas. In…
3D Clothed Human Reconstruction in the Wild
Gyeongsik Moon, Hyeongjin Nam, Takaaki Shiratori +1
Although much progress has been made in 3D clothed human reconstruction, most of the existing methods fail to produce robust results from in-the-wild images, which contain diverse…
CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
Cheeun Hong, Sungyong Baik, Heewon Kim +2
Despite breakthrough advances in image super-resolution (SR) with convolutional neural networks (CNNs), SR has yet to enjoy ubiquitous applications due to the high computational co…
Controllable Image Enhancement
Heewon Kim, Kyoung Mu Lee
Editing flat-looking images into stunning photographs requires skill and time. Automated image enhancement algorithms have attracted increased interest by generating high-quality i…
Pay Attention to Hidden States for Video Deblurring: Ping-Pong Recurrent Neural Networks and Selective Non-Local Attention
JoonKyu Park, Seungjun Nah, Kyoung Mu Lee
Video deblurring models exploit information in the neighboring frames to remove blur caused by the motion of the camera and the objects. Recurrent Neural Networks~(RNNs) are often…
Attentive Fine-Grained Structured Sparsity for Image Restoration
Junghun Oh, Heewon Kim, Seungjun Nah +3
Image restoration tasks have witnessed great performance improvement in recent years by developing large deep models. Despite the outstanding performance, the heavy computation dem…
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image
Reyhaneh Neshatavar, Mohsen Yavartanoo, Sanghyun Son +1
Recently, significant progress has been made on image denoising with strong supervision from large-scale datasets. However, obtaining well-aligned noisy-clean training image pairs…
HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network
JoonKyu Park, Yeonguk Oh, Gyeongsik Moon +2
Hands are often severely occluded by objects, which makes 3D hand mesh estimation challenging. Previous works often have disregarded information at occluded regions. However, we ar…
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network
Wooseok Lee, Sanghyun Son, Kyoung Mu Lee
Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less prac…
Recurrence-in-Recurrence Networks for Video Deblurring
Joonkyu Park, Seungjun Nah, Kyoung Mu Lee
State-of-the-art video deblurring methods often adopt recurrent neural networks to model the temporal dependency between the frames. While the hidden states play key role in delive…
C2N: Practical Generative Noise Modeling for Real-World Denoising
Geonwoon Jang, Wooseok Lee, Sanghyun Son +1
Learning-based image denoising methods have been bounded to situations where well-aligned noisy and clean images are given, or samples are synthesized from predetermined noise mode…
Batch Normalization Tells You Which Filter is Important
Junghun Oh, Heewon Kim, Sungyong Baik +2
The goal of filter pruning is to search for unimportant filters to remove in order to make convolutional neural networks (CNNs) efficient without sacrificing the performance in the…
Generative Residual Attention Network for Disease Detection
Euyoung Kim, Soochahn Lee, Kyoung Mu Lee
Accurate identification and localization of abnormalities from radiology images serve as a critical role in computer-aided diagnosis (CAD) systems. Building a highly generalizable…
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning
Sungyong Baik, Janghoon Choi, Heewon Kim +3
In few-shot learning scenarios, the challenge is to generalize and perform well on new unseen examples when only very few labeled examples are available for each task. Model-agnost…