most citedImproving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling

5 citations · 5 across the 1 of their papers we have counts for

collaborators

3 papers

eess.IV2023★ 2 cited

Pedestrian Accessible Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types

Jiahao Xia, Gavin Gong, Jiawei Liu +2

In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sour…

cs.CV2019★ 5 cited

Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling

Greg Olmschenk, Hao Tang, Zhigang Zhu

Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, manageme…

cs.LG2018

Generalizing semi-supervised generative adversarial networks to regression using feature contrasting

Greg Olmschenk, Zhigang Zhu, Hao Tang

In this work, we generalize semi-supervised generative adversarial networks (GANs) from classification problems to regression problems. In the last few years, the importance of imp…