Search Results for author: Yuan Huang

Found 8 papers, 4 papers with code

End-to-end Rain Streak Removal with RAW Images

no code implementations20 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.

Rain Removal

Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?

no code implementations28 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.

Data Augmentation Translation +1

TextSLAM: Visual SLAM with Semantic Planar Text Features

1 code implementation17 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.

Mixed Reality Scene Understanding

Exploring Representation-Level Augmentation for Code Search

1 code implementation21 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.

Code Search Contrastive Learning +1

Air Quality Prediction Using Improved PSO-BP Neural Network

no code implementations28 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.

Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image

1 code implementation17 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.

Image Denoising

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