91 citations · 124 across the 8 of their papers we have counts for
8 papers
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Benjamin Kiefer, Matej Kristan, Janez Perš +70
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…
Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge
Chih-Chung Hsu, Hsin-Ti Ma
Semantic segmentation is one of the most attractive research fields in computer vision. In the VIPriors challenge, only very limited numbers of training samples are allowed, leadin…
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution
Chih-Chung Hsu, Chia-Hsiang Lin
Deep learning-based single image super-resolution enables very fast and high-visual-quality reconstruction. Recently, an enhanced super-resolution based on generative adversarial n…
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +18
This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting,…
Learning to Detect Fake Face Images in the Wild
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. For example, GAN can be used t…
An Iterative Refinement Approach for Social Media Headline Prediction
Chih-Chung Hsu, Chia-Yen Lee, Ting-Xuan Liao +7
In this study, we propose a novel iterative refinement approach to predict the popularity score of the social media meta-data effectively. With the rapid growth of the social media…
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
Chih-Chung Hsu, Chia-Wen Lin, Weng-Tai Su +1
Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the ident…
CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data
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. To addre…