4.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Seoul National UniversityKR138 papers
- The University of TokyoJP133 papers
- Centre National de la Recherche ScientifiqueFR113 papers
- Pusan National UniversityKR110 papers
- Massachusetts Institute of TechnologyUS105 papers
- Korea Astronomy and Space Science InstituteKR104 papers
- California Institute of TechnologyUS99 papers
- Université Libre de BruxellesBE99 papers
- University of ChicagoUS99 papers
- National Astronomical Observatory of JapanJP97 papers
- Lomonosov Moscow State UniversityRU96 papers
- Universität HamburgDE94 papers
23 papers · 1 filter
Synthetic Realities: Evaluating Human Ability to Distinguish AIGenerated Videos from Real Footage
Danyal Sarfraz, Raja Mubashar Karim, KwanMyung Kim
Recent advances in generative AI have enabled multiple pathways for high-fidelity video synthesis: text-to-video, image-to-video animation, and video outpainting. Empirical side-by…
SVHighlights: Towards Extremely Long Sport Video Highlight Detection
Donggyu Lee, Youngbin Ki, Jeonghun Kang +1
While highlight detection for long-form videos is of great practical importance, most existing methods remain limited to short-form content, largely due to the absence of a suitabl…
Weakly Supervised Cloud Detection Combining Spectral Features and Multi-Scale Deep Network
Shaocong Zhu, Zhiwei Li, Xinghua Li +1
Clouds significantly affect the quality of optical satellite images, which seriously limits their precise application. Recently, deep learning has been widely applied to cloud dete…
Lifelong Person Search
Jae-Won Yang, Seungbin Hong, Jae-Young Sim
Person search is the task to localize a query person in gallery datasets of scene images. Existing methods have been mainly developed to handle a single target dataset only, howeve…
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing
Jeongho Min, Yejun Lee, Dongyoung Kim +1
Recently, reference-based image super-resolution (RefSR) has shown excellent performance in image super-resolution (SR) tasks. The main idea of RefSR is to utilize additional infor…
Human-Object Interaction Prediction in Videos through Gaze Following
Zhifan Ni, Esteve Valls Mascaró, Hyemin Ahn +1
Understanding the human-object interactions (HOIs) from a video is essential to fully comprehend a visual scene. This line of research has been addressed by detecting HOIs from ima…
POEM: Polarization of Embeddings for Domain-Invariant Representations
Sang-Yeong Jo, Sung Whan Yoon
Handling out-of-distribution samples is a long-lasting challenge for deep visual models. In particular, domain generalization (DG) is one of the most relevant tasks that aims to tr…
Robust Human Motion Forecasting using Transformer-based Model
Esteve Valls Mascaro, Shuo Ma, Hyemin Ahn +1
Comprehending human motion is a fundamental challenge for developing Human-Robot Collaborative applications. Computer vision researchers have addressed this field by only focusing…
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation
Jaesung Choe, Kyungdon Joo, Tooba Imtiaz +1
Stereo-LiDAR fusion is a promising task in that we can utilize two different types of 3D perceptions for practical usage -- dense 3D information (stereo cameras) and highly-accurat…
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising
Kanggeun Lee, Won-Ki Jeong
With the advent of advances in self-supervised learning, paired clean-noisy data are no longer required in deep learning-based image denoising. However, existing blind denoising me…
Task-Adaptive Feature Transformer for Few-Shot Segmentation
Jun Seo, Young-Hyun Park, Sung-Whan Yoon +1
Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has…
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency
Hyeonsoo Lee, Won-Ki Jeong
Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation h…
Privacy-Protection Drone Patrol System based on Face Anonymization
Harim Lee, Myeung Un Kim, Yeongjun Kim +2
The robot market has been growing significantly and is expected to become 1.5 times larger in 2024 than what it was in 2019. Robots have attracted attention of security companies t…
Reinforced Coloring for End-to-End Instance Segmentation
Tuan Tran Anh, Khoa Nguyen-Tuan, Tran Minh Quan +1
Instance segmentation is one of the actively studied research topics in computer vision in which many objects of interest should be separated individually. While many feed-forward…
RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth Estimation
Sangyun Oh, Hye-Jin S. Kim, Jongeun Lee +1
We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy c…
Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring
Dongwon Park, Jisoo Kim, Se Young Chun
Multi-scale approach has been used for blind image / video deblurring problems to yield excellent performance for both conventional and recent deep-learning-based state-of-the-art…
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images
Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun
Recently, Stein's unbiased risk estimator (SURE) has been applied to unsupervised training of deep neural network Gaussian denoisers that outperformed classical non-deep learning b…
Efficient architecture for deep neural networks with heterogeneous sensitivity
Hyunjoong Cho, Jinhyeok Jang, Chanhyeok Lee +1
This work presents a neural network that consists of nodes with heterogeneous sensitivity. Each node in a network is assigned a variable that determines the sensitivity with which…
Training Deep Learning Based Denoisers without Ground Truth Data
Shakarim Soltanayev, Se Young Chun
Recently developed deep-learning-based denoisers often outperform state-of-the-art conventional denoisers such as the BM3D. They are typically trained to minimize the mean squared…
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
Tran Minh Quan, Thanh Nguyen-Duc, Won-Ki Jeong
Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on itera…
Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning
Michael S. Ryoo, Kiyoon Kim, Hyun Jong Yang
This paper presents an approach for recognizing human activities from extreme low resolution (e.g., 16x12) videos. Extreme low resolution recognition is not only necessary for anal…
ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Features
Inwan Yoo, David G. C. Hildebrand, Willie F. Tobin +2
The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits. However, each ssEM plane conta…
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution
Michael S. Ryoo, Brandon Rothrock, Charles Fleming +1
Privacy protection from surreptitious video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities an…