Muming Zhao — accepted AI-conference papers

2021

Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling

Kavisha Vidanapathirana, Peyman Moghadam, Ben Harwood, Muming Zhao, Sridha Sridharan, Clinton Fookes

ICRA 2021poster

Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper presents Locus, a novel place recognition method using 3D LiDAR point clouds in large-scale environments. We propose a m…

Cited by 91 Source ↗code ↗Scholar ↗

2020

Weakly Supervised Crowd-Wise Attention For Robust Crowd Counting

Xiyu Kong, Muming Zhao, Hao Zhou, Chongyang Zhang

ICASSP 2020accepted

Due to a wide range of various application scenes, robust crowd counting is still quite difficult and the performance is far from being satisfied. In this paper, we propose a novel robust crowd counting method by introducing a weakly supervised crowd-wise attention network. The proposed work improve…

Cited by 0 Source ↗Scholar ↗

2020

Where, What, Whether: Multi-Modal Learning Meets Pedestrian Detection

Yan Luo, Chongyang Zhang, Muming Zhao, Hao Zhou, Jun Sun

CVPR 2020poster

Pedestrian detection benefits greatly from deep convolutional neural networks (CNNs). However, it is inherently hard for CNNs to handle situations in the presence of occlusion and scale variation. In this paper, we propose W^3Net, which attempts to address above challenges by decomposing the pedestr…

Cited by 39 PDF ↗Scholar ↗

2019

Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting

Muming Zhao, Jian Zhang, Chongyang Zhang, Wenjun Zhang

CVPR 2019poster

Crowd counting is a challenging task in the presence of drastic scale variations, the clutter background, and severe occlusions, etc. Existing CNN-based counting methods tackle these challenges mainly by fusing either multi-scale or multi-context features to generate robust representations. In this…

Cited by 162 PDF ↗Scholar ↗