no code implementations • 20 Dec 2023 • Guodong Du, HaoJian Deng, Jiahao Su, Yuan Huang
To be specific, we generate rainy RAW data by converting color rain streak into RAW space and design simple but efficient RAW processing algorithms to synthesize both rainy and clean color images.
no code implementations • 28 Nov 2023 • Wang Zhu, Ishika Singh, Yuan Huang, Robin Jia, Jesse Thomason
Data augmentation via back-translation is common when pretraining Vision-and-Language Navigation (VLN) models, even though the generated instructions are noisy.
1 code implementation • 17 May 2023 • Boying Li, Danping Zou, Yuan Huang, Xinghan Niu, Ling Pei, Wenxian Yu
The results show that integrating texture features leads to a more superior SLAM system that can match images across day and night.
1 code implementation • 21 Oct 2022 • Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, Yanlin Wang
In this paper, we explore augmentation methods that augment data (both code and query) at representation level which does not require additional data processing and training, and based on this we propose a general format of representation-level augmentation that unifies existing methods.
1 code implementation • ICCV 2021 • Boying Li, Yuan Huang, Zeyu Liu, Danping Zou, Wenxian Yu
Inspired by the early works on indoor modeling, we leverage the structural regularities exhibited in indoor scenes, to train a better depth network.
no code implementations • 20 Jan 2021 • Tao Wei, Angelica I Aviles-Rivero, Shuo Wang, Yuan Huang, Fiona J Gilbert, Carola-Bibiane Schönlieb, Chang Wen Chen
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
Cancer-no cancer per image classification Image Classification +3
no code implementations • 28 May 2020 • Yuan Huang, YUXING XIANG, RUIXIAO ZHAO, AND ZHE CHEN
Predicting urban air quality is a significant aspect of preventing urban air pollution and improving the living environment of urban residents.
1 code implementation • 17 Jun 2019 • Jun Xu, Yuan Huang, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, Ling Shao
A simple but useful observation on our NAC is: as long as the noise is weak, it is feasible to learn a self-supervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images.