Taeoh Kim

Bio

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I am a Researcher & Engineer in NAVER Cloud Corporation.

Before joining NAVER, I obtained my Ph.D. in Electronic Engineering at Yonsei University under the supervision of Prof. Sangyoun Lee.

I conduct research and development on technologies for Video Understanding and Generation, and provide technical support for related services.

Also, we are always looking for an Intern interested in Video-related research. Please feel free to contact me.

News

Publications

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CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images

Jungho Lee, Suhwan Cho, Taeoh Kim, Ho-Deok Jang, Minhyeok Lee, Geonho Cha, Dongyoon Wee, Dogyoon Lee, Sangyoun Lee

CVPR 2025

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Towards Multi-Domain Learning for Generalizable Video Anomaly Detection

MyeongAh Cho, Taeoh Kim, Minho Shim, Dongyoon Wee, Sangyoun Lee

NeurIPS 2024

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Classification Matters: Improving Video Action Detection with Class-Specific Attention

Jinsung Lee, Taeoh Kim, Inwoong Lee, Minho Shim, Dongyoon Wee, Minsu Cho, Suha Kwak

ECCV 2024 (Oral)

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A Nonlinear, Regularized, and Data-independent Modulation for Continuously Interactive Image Processing Network

Hyeongmin Lee, Taeoh Kim, Hanbin Son, Sangwook Baek, Minsu Cheon, Sangyoun Lee

Internation Journal of Computer Vision 2023 (IJCV, IF 11.6)

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Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection

Pilhyeon Lee, Taeoh Kim, Minho Shim, Dongyoon Wee, Hyeran Byun

CVPR 2023

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Exploring Temporally Dynamic Data Augmentation for Video Recognition

Taeoh Kim, Jinhyung Kim, Minho Shim, Sangdoo Yun, Myunggu Kang, Dongyoon Wee, Sangyoun Lee

ICLR 2023 (Spotlight)

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Frequency Selective Augmentation for Video Representation Learning

Jinhyung Kim, Taeoh Kim, Minho Shim, Dongyoon Han, Dongyoon Wee, Junmo Kim

AAAI 2023 (Oral)

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Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features

MyeongAh Cho, Taeoh Kim, Woo Jin Kim, Suhwan Cho, Sangyoun Lee

Pattern Recognition 2022 (PR, IF 7.5)

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Geometry-Aware Deep Video Deblurring via Recurrent Feature Refinement

Taeoh Kim, Sangyoun Lee

IEEE Transactions on Image Processing 2022 (IEEE TIP, IF 10.8)

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Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks

Hanbin Son, Taeoh Kim, Hyeongmin Lee, Sangyoun Lee

IEEE Transactions on Image Processing 2021 (IEEE TIP, IF 10.8)

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Block-Attentive Subpixel Prediction Networks for Computationally Efficient Image Restoration

Taeoh Kim, Chajin Shin, Sangjin Lee, Sangyoun Lee

IEEE Access 2021 (IF 3.4)

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Test-Time Adaptation for Out-of-distributed Image Inpainting

Chajin Shin, Taeoh Kim, Sangjin Lee, Sangyoun Lee

ICIP 2021

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Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition

Taeoh Kim*, Hyeongmin Lee*, MyeongAh Cho*, Ho Seong Lee, Dong Heon Cho, Sangyoun Lee

ECCV Workshop 2020

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Relational Deep Feature Learning for Heterogeneous Face Recognition

MyeongAh Cho, Taeoh Kim, Ig-Jae Kim, Kyungjae Lee, Sangyoun Lee

IEEE Transactions on Information Forensics and Security 2020 (IEEE TIFS, IF 6.3)

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Extrapolative-Interpolative Cycle-Consistency Learning For Video Frame Extrapolation

Sangjin Lee, Hyeongmin Lee, Taeoh Kim, Sangyoun Lee

ICIP 2020

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AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation

Hyeongmin Lee, Taeoh Kim, Tae-Young Chung, Daehyun Pak, Yuseok Ban, Sangyoun Lee

CVPR 2020

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Sampling Operator to Learn the Scalable Correlation Filter for Visual Tracking

Minkyu Lee, Taeoh Kim, Yuseok Ban, Eungyeol Song, Sangyoun Lee

IEEE Access 2019 (IF 3.4)

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SF-CNN: A Fast Compression Artifacts Removal via Spatial-To-Frequency Convolutional Neural Networks

Taeoh Kim, Hyeongmin Lee, Hanbin Son, Sangyoun Lee

ICIP 2019

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NIR-to-VIS Face Recognition via Embedding Relations and Coordinates of the Pairwise Features

MyeongAh Cho, Tae-Young Chung, Taeoh Kim, Sangyoun Lee

ICB 2019

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Collabonet: Collaboration of Generative Models by Unsupervised Classification

Hyeongmin Lee, Taeoh Kim, Eungyeol Song, Sangyoun Lee

ICIP 2018

Contacts