Chih-Chung Hsu | DeepAI

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∙ 03/15/2023

Strong Baseline and Bag of Tricks for COVID-19 Detection of CT Scans

This paper investigates the application of deep learning models for lung...

0 Chih-Chung Hsu, et al. ∙

∙ 07/04/2022

Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans

Computed tomography (CT) imaging could be very practical for diagnosing ...

0 Chih-Chung Hsu, et al. ∙

∙ 02/16/2022

ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

Age-related macular degeneration (AMD) is the leading cause of visual im...

11 Huihui Fang, et al. ∙

∙ 07/12/2021

Visual Transformer with Statistical Test for COVID-19 Classification

With the massive damage in the world caused by Coronavirus Disease 2019 ...

0 Chih-Chung Hsu, et al. ∙

∙ 07/17/2020

Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge

Semantic segmentation is one of the most attractive research fields in c...

0 Chih-Chung Hsu, et al. ∙

∙ 11/20/2019

Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution

Deep learning-based single image super-resolution enables very fast and ...

0 Chih-Chung Hsu, et al. ∙

∙ 11/18/2019

AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results

This paper reviews the AIM 2019 challenge on real world super-resolution...

12 Andreas Lugmayr, et al. ∙

∙ 09/24/2018

Learning to Detect Fake Face Images in the Wild

Although Generative Adversarial Network (GAN) can be used to generate th...

1 Chih-Chung Hsu, et al. ∙

∙ 09/24/2018

An Iterative Refinement Approach for Social Media Headline Prediction

In this study, we propose a novel iterative refinement approach to predi...

0 Chih-Chung Hsu, et al. ∙

∙ 07/22/2018

SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination

Despite generative adversarial networks (GANs) can hallucinate photo-rea...

0 Chih-Chung Hsu, et al. ∙

∙ 05/19/2017

CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data

Given a large unlabeled set of images, how to efficiently and effectivel...

0 Chih-Chung Hsu, et al. ∙

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