activity
20132026
most citedOn the use of deep learning for phase recovery

235 citations · 659 across the 25 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

cs.CY2022★ 3 cited

PATE: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction

Yaping Zhao, Ramgopal Ravi, Shuhui Shi +3

Real estate prices have a significant impact on individuals, families, businesses, and governments. The general objective of real estate price prediction is to identify and exploit…

cs.CV2022★ 60 cited

LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images

Shansi Zhang, Nan Meng, Edmund Y. Lam

Light field (LF) images containing information for multiple views have numerous applications, which can be severely affected by low-light imaging. Recent learning-based methods for…

cs.CY2022

H4M: Heterogeneous, Multi-source, Multi-modal, Multi-view and Multi-distributional Dataset for Socioeconomic Analytics in the Case of Beijing

Yaping Zhao, Shuhui Shi, Ramgopal Ravi +3

The study of socioeconomic status has been reformed by the availability of digital records containing data on real estate, points of interest, traffic and social media trends such…

cs.CV2022★ 2 cited

Multimodal Transformer for Automatic 3D Annotation and Object Detection

Chang Liu, Xiaoyan Qian, Binxiao Huang +4

Despite a growing number of datasets being collected for training 3D object detection models, significant human effort is still required to annotate 3D boxes on LiDAR scans. To aut…

cs.CV2022★ 2 cited

MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator

Chang Liu, Xiaoyan Qian, Xiaojuan Qi +3

Manually annotating 3D point clouds is laborious and costly, limiting the training data preparation for deep learning in real-world object detection. While a few previous studies t…

cs.CV2022

Point Cloud Denoising via Momentum Ascent in Gradient Fields

Yaping Zhao, Haitian Zheng, Zhongrui Wang +2

To achieve point cloud denoising, traditional methods heavily rely on geometric priors, and most learning-based approaches suffer from outliers and loss of details. Recently, the g…