Understanding the impact of mistakes on background regions in crowd counting

30 March 2020

Papers citing "Understanding the impact of mistakes on background regions in crowd counting"

7 / 7 papers shown

Title
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting Mingyue Guo Li Yuan Zhaoyi Yan Binghui Chen Yaowei Wang QiXiang Ye 25 4 0 04 Dec 2023
Training-free Object Counting with Prompts Zenglin Shi Ying Sun Mengmi Zhang VLM 18 20 0 30 Jun 2023
Focus for Free in Density-Based Counting Zenglin Shi Pascal Mettes Cees G. M. Snoek 3DPC 20 8 0 08 Jun 2023
HDNet: A Hierarchically Decoupled Network for Crowd Counting Chenliang Gu Changan Wang Bin-Bin Gao Jun Liu Tianliang Zhang 14 0 0 12 Dec 2022
Counting with Adaptive Auxiliary Learning Y. Meng J. Bridge Meng Wei Yitian Zhao Yihong Qiao Xiaoyun Yang Xiaowei Huang Yalin Zheng 16 4 0 08 Mar 2022
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal Haoyue Bai Jiageng Mao Shueng-Han Gary Chan 25 22 0 31 Dec 2020
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi Jose Caballero Ferenc Huszár J. Totz Andrew P. Aitken Rob Bishop Daniel Rueckert Zehan Wang SupR 190 5,173 0 16 Sep 2016