Search Results for author: Chih-Chung Hsu

Found 12 papers, 4 papers with code

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

no code implementations24 Nov 2022 Benjamin Kiefer, Matej Kristan, Janez Perš, Lojze Žust, Fabio Poiesi, Fabio Augusto de Alcantara Andrade, Alexandre Bernardino, Matthew Dawkins, Jenni Raitoharju, Yitong Quan, Adem Atmaca, Timon Höfer, Qiming Zhang, Yufei Xu, Jing Zhang, DaCheng Tao, Lars Sommer, Raphael Spraul, Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, Jan Lukas Augustin, Eui-ik Jeon, Impyeong Lee, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Sagar Verma, Siddharth Gupta, Shishir Muralidhara, Niharika Hegde, Daitao Xing, Nikolaos Evangeliou, Anthony Tzes, Vojtěch Bartl, Jakub Špaňhel, Adam Herout, Neelanjan Bhowmik, Toby P. Breckon, Shivanand Kundargi, Tejas Anvekar, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudengudi, Arpita Vats, Yang song, Delong Liu, Yonglin Li, Shuman Li, Chenhao Tan, Long Lan, Vladimir Somers, Christophe De Vleeschouwer, Alexandre Alahi, Hsiang-Wei Huang, Cheng-Yen Yang, Jenq-Neng Hwang, Pyong-Kun Kim, Kwangju Kim, Kyoungoh Lee, Shuai Jiang, Haiwen Li, Zheng Ziqiang, Tuan-Anh Vu, Hai Nguyen-Truong, Sai-Kit Yeung, Zhuang Jia, Sophia Yang, Chih-Chung Hsu, Xiu-Yu Hou, Yu-An Jhang, Simon Yang, Mau-Tsuen Yang

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.

Object object-detection +2

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

no code implementations4 Jul 2022 Chih-Chung Hsu, Chi-Han Tsai, Guan-Lin Chen, Sin-Di Ma, Shen-Chieh Tai

However, the nature of the CT images is even more diverse since the resolution and number of the slices of a CT scan are determined by the machine and its settings.

Computed Tomography (CT) Representation Learning

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

no code implementations16 Feb 2022 Huihui Fang, Fei Li, Huazhu Fu, Xu sun, Xingxing Cao, Fengbin Lin, Jaemin Son, Sunho Kim, Gwenole Quellec, Sarah Matta, Sharath M Shankaranarayana, Yi-Ting Chen, Chuen-heng Wang, Nisarg A. Shah, Chia-Yen Lee, Chih-Chung Hsu, Hai Xie, Baiying Lei, Ujjwal Baid, Shubham Innani, Kang Dang, Wenxiu Shi, Ravi Kamble, Nitin Singhal, Ching-Wei Wang, Shih-Chang Lo, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu, iChallenge-AMD study group

The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.

Visual Transformer with Statistical Test for COVID-19 Classification

no code implementations12 Jul 2021 Chih-Chung Hsu, Guan-Lin Chen, Mei-Hsuan Wu

The frame-level feature is extracted from each CT slice based on any backbone network and followed by feeding the features to our within-slice-Transformer (WST) to discover the context information in the pixel dimension.

Classification Computed Tomography (CT) +1

Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge

no code implementations17 Jul 2020 Chih-Chung Hsu, Hsin-Ti Ma

Then, the two-dimensional cross-entropy loss is adopted to calculate the loss between the predicted edge map and its ground truth, termed as an edge-preserving loss.

Segmentation Semantic Segmentation

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

no code implementations20 Nov 2019 Chih-Chung Hsu, Chia-Hsiang Lin

Recently, an enhanced super-resolution based on generative adversarial network (ESRGAN) has achieved excellent performance in terms of both qualitative and quantitative quality of the reconstructed high-resolution image.

Generative Adversarial Network Image Super-Resolution +1

Learning to Detect Fake Face Images in the Wild

1 code implementation24 Sep 2018 Chih-Chung Hsu, Chia-Yen Lee, Yi-Xiu Zhuang

Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns.

Face Swapping GAN image forensics +2

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

1 code implementation22 Jul 2018 Chih-Chung Hsu, Chia-Wen Lin, Weng-Tai Su, Gene Cheung

Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable.

Face Hallucination Face Reconstruction +3

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

no code implementations19 May 2017 Chih-Chung Hsu, Chia-Wen Lin

Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem.

Clustering Representation Learning

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