Search Results for author: Taewoo Kim

Found 15 papers, 4 papers with code

RobustSwap: A Simple yet Robust Face Swapping Model against Attribute Leakage

no code implementations28 Mar 2023 Jaeseong Lee, Taewoo Kim, Sunghyun Park, Younggun Lee, Jaegul Choo

However, we observed that previous approaches still suffer from source attribute leakage, where the source image's attributes interfere with the target image's.

Attribute Face Swapping

Non-Coaxial Event-Guided Motion Deblurring with Spatial Alignment

no code implementations ICCV 2023 Hoonhee Cho, Yuhwan Jeong, Taewoo Kim, Kuk-Jin Yoon

Motion deblurring from a blurred image is a challenging computer vision problem because frame-based cameras lose information during the blurring process.

Deblurring Image Deblurring

Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator

no code implementations18 Nov 2022 Ho Suk, Taewoo Kim, Hyungbin Park, Pamul Yadav, Junyong Lee, Shiho Kim

Despite the rapid improvement of autonomous driving technology in recent years, automotive manufacturers must resolve liability issues to commercialize autonomous passenger car of SAE J3016 Level 3 or higher.

Autonomous Driving

Morphology-Aware Interactive Keypoint Estimation

1 code implementation15 Sep 2022 Jinhee Kim, Taesung Kim, Taewoo Kim, Jaegul Choo, Dong-Wook Kim, Byungduk Ahn, In-Seok Song, Yoon-Ji Kim

To fully automate this procedure, deep-learning-based methods have been widely proposed and have achieved high performance in detecting keypoints in medical images.

Keypoint Estimation

Event-guided Deblurring of Unknown Exposure Time Videos

no code implementations13 Dec 2021 Taewoo Kim, Jeongmin Lee, Lin Wang, Kuk-Jin Yoon

To this end, we first derive a new formulation for event-guided motion deblurring by considering the exposure and readout time in the video frame acquisition process.

Deblurring

Ambiguity Adaptive Inference and Single-shot based Channel Pruning for Satellite Processing Environments

no code implementations29 Sep 2021 Minsu Jeon, Kyungno Joo, Changha Lee, Taewoo Kim, SeongHwan Kim, Chan-Hyun Youn

In a restricted computing environment like satellite on-board systems, running DL models has limitation on high-speed processing due to the problems such as restriction of available power to consume compared to the relatively high computational complexity.

K-Hairstyle: A Large-scale Korean Hairstyle Dataset for Virtual Hair Editing and Hairstyle Classification

no code implementations11 Feb 2021 Taewoo Kim, Chaeyeon Chung, Sunghyun Park, Gyojung Gu, Keonmin Nam, Wonzo Choe, Jaesung Lee, Jaegul Choo

In response, we introduce a novel large-scale Korean hairstyle dataset, K-hairstyle, containing 500, 000 high-resolution images.

Translation

Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles

no code implementations12 Jan 2020 Il Bae, Jaeyoung Moon, Junekyo Jhung, Ho Suk, Taewoo Kim, Hyungbin Park, Jaekwang Cha, Jinhyuk Kim, Dohyun Kim, Shiho Kim

Moreover, we propose a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles.

Autonomous Vehicles

Cut-and-Paste Dataset Generation for Balancing Domain Gaps in Object Instance Detection

no code implementations26 Sep 2019 Woo-han Yun, Taewoo Kim, Jaeyeon Lee, Jaehong Kim, Junmo Kim

Then, we show that the original cut-and-paste approach suffers from a new domain gap problem, an unbalanced domain gaps, because it has two separate source domains for foreground and background, unlike the conventional domain shift problem.

Domain Adaptation Generative Adversarial Network +2

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

3 code implementations25 Sep 2019 Taewoo Kim, Joo-Haeng Lee

The motion retargeting learning is performed using refined data in a latent space by the cyclic and filtering paths of our method.

motion retargeting reinforcement-learning +1

On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods

no code implementations25 Sep 2019 Heejae Kim, Taewoo Kim, Chan-Hyun Youn

Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e. g., mobile-cloud ecosystems.

Federated Learning Hyperparameter Optimization

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