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Class Concept Representation From Contextual Texts for Training-Free Multi-Label Recognition
The power of large vision-language models (VLMs) has been demonstrated for downstream vision tasks, including multi-label recognition (MLR) with a training-free approach or prompt tuning by measuring the cosine similarity between the text features related to class names and the visual features of the images. Previous studies typically represent class-related text features by averaging simple handc...Show More
Zero-shot Diffusion Models for Demosaicing in Bayer and Non-Bayer Image Sensors
Haechang Lee;Dong Ju Mun;Joohoon Lee;Hyunwoo Lee;Se Young Chun
2025 International Conference on Electronics, Information, and Communication (ICEIC)
Year: 2025 | Conference Paper |
As the demand for high-quality, high-resolution images on mobile phones increases, modern devices now incorporate non-Bayer image sensors that maintain some characteristics of the traditional Bayer pattern while introducing new variations. This evolution has increased the complexity of restoring sensor raw images to full RGB using image signal processors (ISPs), revealing the limitations of conven...Show More
Few-Shot Arbitrary Super-Resolution Considering Consistency and Realness
2025 International Conference on Electronics, Information, and Communication (ICEIC)
Year: 2025 | Conference Paper |
Super-resolution (SR) is the task of reconstructing visually natural high-resolution (HR) images from low-resolution (LR) images. This field has made significant advancements with the development of deep learning, and recent research has focused on arbitrary-scale super-resolution, where a single model performs upsampling across continuous scales. However, most existing studies on arbitrary-scale ...Show More
Focusing on Representation of Multi-head Attention for Open-Vocabulary Semantic Segmentation
2025 International Conference on Electronics, Information, and Communication (ICEIC)
Year: 2025 | Conference Paper |
With the advancement of large vision-language models, impressive results have been achieved in open-vocabulary tasks that require strong generalization ability. However, in pixel-level dense prediction tasks, recognizing the local information of objects remains challenging. This is because query-key multiplication in self-attention is not well-suited for dense prediction. Instead, significant perf...Show More
Image quality assessment (IQA) is a fundamental task in computer vision with the goal of accurately predicting the mean opinion score of humans for assessing the quality of images. While recent advances in deep neural networks (DNNs) have sparked much research on IQA, with the hope for IQA to mimic humans effectively, there has been a lack of systematic and analytical research on understanding wha...Show More
Deep Network Regularization for Phase-Based Magnetic Resonance Electrical Properties Tomography With Stein's Unbiased Risk Estimator
Chuanjiang Cui;Kyu-Jin Jung;Mohammed A. Al-Masni;Jun-Hyeong Kim;Soo-Yeon Kim;Mina Park;Shao Ying Huang;Se Young Chun;Dong-Hyun Kim
Magnetic resonance imaging (MRI) can estimate tissue conductivity values using phase-based magnetic resonance electrical properties tomography (MR-EPT). However, this method is prone to noise amplification due to the Laplacian operator's sensitivity. To address this issue, we propose a novel unsupervised preprocessing denoiser for MRI transceive phase images. Our approach draws inspiration from th...Show More
Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases, there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data are scarce and diversity is large on the one hand, and on the other, there is a lot of structure in the data that can be explo...Show More
On Exact Inversion of DPM-Solvers
Seongmin Hong;Kyeonghyun Lee;Suh Yoon Jeon;Hyewon Bae;Se Young Chun
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality signifi-cantly, but have posed challenges to find the exact inverse (i.e., finding the initial noise from the given image). Here we investigate the exact inversions for DPM-solvers and pro-pose algorithms to perform them when samples are gener-ated b...Show More
PODIA-3D: Domain Adaptation of 3D Generative Model Across Large Domain Gap Using Pose-Preserved Text-to-Image Diffusion
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
Recently, significant advancements have been made in 3D generative models, however training these models across diverse domains is challenging and requires an huge amount of training data and knowledge of pose distribution. Text-guided domain adaptation methods have allowed the generator to be adapted to the target domains using text prompts, thereby obviating the need for assembling numerous data...Show More
Online Continual Learning on Hierarchical Label Expansion
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Continual learning (CL) enables models to adapt to new tasks and environments without forgetting previously learned knowledge. While current CL setups have ignored the relationship between labels in the past task and the new task with or without small task overlaps, real-world scenarios often involve hierarchical relationships between old and new tasks, posing another challenge for traditional CL ...Show More
On the Robustness of Normalizing Flows for Inverse Problems in Imaging
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Conditional normalizing flows can generate diverse image samples for solving inverse problems. Most normalizing flows for inverse problems in imaging employ the conditional affine coupling layer that can generate diverse images quickly. However, unintended severe artifacts are occasionally observed in the output of them. In this work, we address this critical issue by investigating the origins of ...Show More
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors
Haechang Lee;Dongwon Park;Wongi Jeong;Kijeong Kim;Hyunwoo Je;Dongil Ryu;Se Young Chun
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
As the physical size of recent CMOS image sensors (CIS) gets smaller, the latest mobile cameras adopt unique non-Bayer color filter array (CFA) patterns (e.g., Quad, Nona, Q×Q), which consist of homogeneous color units with adjacent pixels. These non-Bayer CFAs are superior to conventional Bayer CFA thanks to their changeable pixel-bin sizes for different light conditions, but may introduce visual...Show More
All-in-One Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (20)
Image restorations for single degradations have been widely studied, demonstrating excellent performance for each degradation, but can not reflect unpredictable realistic environments with unknown multiple degradations, which may change over time. To mitigate this issue, image restorations for known and unknown multiple degradations have recently been investigated, showing promising results, but r...Show More
DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (15)
Recent 3D generative models have achieved remarkable performance in synthesizing high resolution photorealistic images with view consistency and detailed 3D shapes, but training them for diverse domains is challenging since it requires massive training images and their camera distribution information. Text-guided domain adaptation methods have shown impressive performance on converting the 2D gene...Show More
Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT
Hanvit Kim;Zongyu Li;Jiye Son;Jeffrey A. Fessler;Yuni K. Dewaraja;Se Young Chun
Accurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Monte-Carlo (MC) simulation can yield accurate scatter estimation, but is computationally expensive. Recent deep learning-based approaches can yield accurate scatter estimates quickly, yet full MC simulation is still required to generate scatter estimat...Show More
Domain adaptation from posteroanterior to anteroposterior X-ray radiograph classification via deep neural converter with label recycling
Dongwon Park;Minjun Kim;Hyungjin Kim;Jongho Lee;Se Young Chun
2023 International Conference on Electronics, Information, and Communication (ICEIC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Deep neural network trained with massive labeled dataset has shown impressive performance in multiple disease classification tasks for chest X-ray radiograph (CXR). However, different imaging protocols even for the same disease such as posteroanterior (PA) and anteroposterior (AP) CXR often lead to domain gap, causing significant performance degradation and requiring more images with painstaking l...Show More
Multi-Scale Curriculum Learning for Efficient Automatic Whole Slide Image Segmentation
2022 IEEE International Conference on Big Data and Smart Computing (BigComp)
Year: 2022 | Conference Paper |
Deep learning becomes a powerful tool for multiple tasks in computational pathology and has achieved remarkable performance in automatic tumor segmentation of whole slide images (WSIs). Curriculum learning is an effective learning strategy that trains a deep neural network from easy to hard samples and has been applied to the problem of tumor segmentations in WSIs. However, it requires measuring t...Show More
Image Restoration by Deep Projected GSURE
Shady Abu-Hussein;Tom Tirer;Se Young Chun;Yonina C. Eldar;Raja Giryes
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2022 | Conference Paper |
Cited by: Papers (6)
Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. In recent years, solutions that are based on deep Convolutional Neural Networks (CNNs) have shown great promise. Yet, most of these techniques, which train CNNs using external data, are restricted to the observation models that have been used in the training phase. A recent alternative...Show More
Rethinking Deep Image Prior for Denoising
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (25)
Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping. To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress and propose a principled stopping criterio...Show More
Task-Aware Variational Adversarial Active Learning
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (75)
Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques. Active learning (AL) tackles this by querying the most informative samples to be annotated among unlabeled pool. Two promising directions for AL that have been recently explored are task-agnostic approach to select data points that are far from the current labe...Show More
Empirically Accelerating Scaled Gradient Projection Using Deep Neural Network for Inverse Problems in Image Processing
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Recently, deep neural networks (DNNs) have shown advantages in accelerating optimization algorithms. One approach is to unfold finite number of iterations of conventional optimization algorithms and to learn parameters in the algorithms. However, these are forward methods and are indeed neither iterative nor convergent. Here, we present a novel DNN-based convergent iterative algorithm that acceler...Show More
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module
2020 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2020 | Conference Paper |
Cited by: Papers (26)
Rotation invariance has been an important topic in computer vision tasks. Ideally, robot grasp detection should be rotation-invariant. However, rotation-invariance in robotic grasp detection has been only recently studied by using rotation anchor box that are often time-consuming and unreliable for multiple objects. In this paper, we propose a rotation ensemble module (REM) for robotic grasp detec...Show More
A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection
Dongwon Park;Yonghyeok Seo;Dongju Shin;Jaesik Choi;Se Young Chun
2020 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2020 | Conference Paper |
Cited by: Papers (30)
Applications of deep neural network (DNN) based object and grasp detections could be expanded significantly when the network output is processed by a high-level reasoning over relationship of objects. Recently, robotic grasp detection and object detection with reasoning have been investigated using DNNs. There have been efforts to combine these multitasks using separate networks so that robots can...Show More
Preliminary Studies On Training And Fine-Tuning Deep Denoiser Neural Networks In Learned D-Amp For Undersampled Real Mr Measurements
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)
Year: 2020 | Conference Paper |
Recently, deep learning based MR image reconstructions have shown outstanding performance. While there have been many direct mapping based methods by deep neural networks without taking advantage of known physical model of medical imaging modality, some groups investigated combining conventional model-based image reconstruction (MBIR) and learning based method to enhance performance and computatio...Show More
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Namhyuk Ahn;Dongwoon Bai;Jie Cai;Yun Cao;Junyang Chen;Kaihua Cheng;SeYoung Chun;Wei Deng;Mostafa El-Khamy;Chiu Man Ho;Xiaozhong Ji;Amin Kheradmand;Gwantae Kim;Hanseok Ko;Kanghyu Lee;Jungwon Lee;Hao Li;Ziluan Liu;Zhi-Song Liu;Shuai Liu;Yunhua Lu;Zibo Meng;Pablo Navarrete Michelini;Christian Micheloni;Kalpesh Prajapati;Haoyu Ren;Yong Hyeok Seo;Wan-Chi Siu;Kyung-Ah Sohn;Ying Tai;Rao Muhammad Umer;Shuangquan Wang;Huibing Wang;Timothy Haoning Wu;Haoning Wu;Biao Yang;Fuzhi Yang;Jaejun Yoo;Tongtong Zhao;Yuanbo Zhou;Haijie Zhuo;Ziyao Zong;Xueyi Zou
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (98)
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Proc...Show More
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Namhyuk Ahn;Dongwoon Bai;Jie Cai;Yun Cao;Junyang Chen;Kaihua Cheng;SeYoung Chun;Wei Deng;Mostafa El-Khamy;Chiu Man Ho;Xiaozhong Ji;Amin Kheradmand;Gwantae Kim;Hanseok Ko;Kanghyu Lee;Jungwon Lee;Hao Li;Ziluan Liu;Zhi-Song Liu;Shuai Liu;Yunhua Lu;Zibo Meng;Pablo Navarrete Michelini;Christian Micheloni;Kalpesh Prajapati;Haoyu Ren;Yong Hyeok Seo;Wan-Chi Siu;Kyung-Ah Sohn;Ying Tai;Rao Muhammad Umer;Shuangquan Wang;Huibing Wang;Timothy Haoning Wu;Haoning Wu;Biao Yang;Fuzhi Yang;Jaejun Yoo;Tongtong Zhao;Yuanbo Zhou;Haijie Zhuo;Ziyao Zong;Xueyi Zou
Year: 2020 | Conference Paper |
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