Search Results for author: Sungho Suh

Found 16 papers, 3 papers with code

A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image

no code implementations24 Feb 2023 Jwalin Bhatt, Yaroslav Zharov, Sungho Suh, Tilo Baumbach, Vincent Heuveline, Paul Lukowicz

Morphological atlases are an important tool in organismal studies, and modern high-throughput Computed Tomography (CT) facilities can produce hundreds of full-body high-resolution volumetric images of organisms.

Computed Tomography (CT) Image Segmentation +3

PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation

no code implementations1 Feb 2023 Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Paul Lukowicz

Ground pressure exerted by the human body is a valuable source of information for human activity recognition (HAR) in unobtrusive pervasive sensing.

Human Activity Recognition

AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation

no code implementations20 Nov 2022 Hyungmin Kim, Sungho Suh, SungHyun Baek, Daehwan Kim, Daun Jeong, Hansang Cho, Junmo Kim

Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning.

Self-Knowledge Distillation

Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition

no code implementations4 Oct 2022 Vitor Fortes Rey, Sungho Suh, Paul Lukowicz

To mitigate this problem we propose a method that facilitates the use of information from sensors that are only present during the training process and are unavailable during the later use of the system.

Activity Recognition

TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation

no code implementations14 Sep 2022 Sungho Suh, Vitor Fortes Rey, Paul Lukowicz

In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features.

Human Activity Recognition Self-Knowledge Distillation

Estimation of 3D Body Shape and Clothing Measurements from Frontal- and Side-view Images

no code implementations28 May 2022 Kundan Sai Prabhu Thota, Sungho Suh, Bo Zhou, Paul Lukowicz

The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several conditions, such as lack of publicly available realistic datasets, ambiguity in multiple camera resolutions, and the undefinable human shape space.

Virtual Try-on

Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition

no code implementations23 Oct 2021 Sungho Suh, Vitor Fortes Rey, Paul Lukowicz

The proposed network is based on the adversarial encoder-decoder structure with the MMD realign the data distribution over multiple subjects.

Human Activity Recognition

Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks

1 code implementation26 Sep 2021 Sungho Suh, Paul Lukowicz, Yong Oh Lee

The experimental results show that the proposed feature extraction method can effectively predict the RUL and outperforms the conventional RUL prediction approaches based on deep neural networks.

Sequential Targeting: an incremental learning approach for data imbalance in text classification

no code implementations20 Nov 2020 Joel Jang, Yoonjeon Kim, Kyoungho Choi, Sungho Suh

Classification tasks require a balanced distribution of data to ensure the learner to be trained to generalize over all classes.

General Classification Incremental Learning +2

Discriminative feature generation for classification of imbalanced data

1 code implementation24 Oct 2020 Sungho Suh, Paul Lukowicz, Yong Oh Lee

In this paper, we propose a novel supervised discriminative feature generation (DFG) method for a minority class dataset.

Classification Data Augmentation +1

Two-stage generative adversarial networks for document image binarization with color noise and background removal

1 code implementation20 Oct 2020 Sungho Suh, Jihun Kim, Paul Lukowicz, Yong Oh Lee

Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition.

Binarization Image Enhancement

Fusion of Global-Local Features for Image Quality Inspection of Shipping Label

no code implementations26 Aug 2020 Sungho Suh, Paul Lukowicz, Yong Oh Lee

These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.

object-detection Object Detection

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