Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting

no code implementations • 12 Mar 2022 • Fusen Wang, Kai Liu, Fei Long, Nong Sang, Xiaofeng Xia, Jun Sang

However, the transformer directly partitions the crowd images into a series of tokens, which may not be a good choice due to each pedestrian being an independent individual, and the parameter number of the network is very large.

Crowd Counting Weakly-supervised Learning

Hybrid attention network based on progressive embedding scale-context for crowd counting

no code implementations • 4 Jun 2021 • Fusen Wang, Jun Sang, Zhongyuan Wu, Qi Liu, Nong Sang

In this paper, we propose a Hybrid Attention Network (HAN) by employing Progressive Embedding Scale-context (PES) information, which enables the network to simultaneously suppress noise and adapt head scale variation.

Crowd Counting