15 citations · 31 across the 11 of their papers we have counts for
7 papers · 1 filter
Scene-Adaptive Attention Network for Crowd Counting
Xing Wei, Yuanrui Kang, Jihao Yang +4
In recent years, significant progress has been made on the research of crowd counting. However, as the challenging scale variations and complex scenes existed in crowds, neither tr…
Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay -- 3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A Continual Object Classification
Muhammad Rifki Kurniawan, Xing Wei, Yihong Gong
Online continual learning in the wild is a very difficult task in machine learning. Non-stationarity in online continual learning potentially brings about catastrophic forgetting i…
Anomaly Detection via Self-organizing Map
Ning Li, Kaitao Jiang, Zhiheng Ma +3
Anomaly detection plays a key role in industrial manufacturing for product quality control. Traditional methods for anomaly detection are rule-based with limited generalization abi…
Direct Measure Matching for Crowd Counting
Hui Lin, Xiaopeng Hong, Zhiheng Ma +4
Traditional crowd counting approaches usually use Gaussian assumption to generate pseudo density ground truth, which suffers from problems like inaccurate estimation of the Gaussia…
InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation
Dahu Shi, Xing Wei, Xiaodong Yu +3
Multi-person pose estimation is an attractive and challenging task. Existing methods are mostly based on two-stage frameworks, which include top-down and bottom-up methods. Two-sta…
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration
Yuhang He, Wentao Yu, Jie Han +3
In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation. Most existing MOT methods track multiple objects using a singular RGB…