15 citations · 31 across the 11 of their papers we have counts for
11 papers
Knowledge Restore and Transfer for Multi-label Class-Incremental Learning
Songlin Dong, Haoyu Luo, Yuhang He +2
Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application…
Deep Dynamic Scene Deblurring from Optical Flow
Jiawei Zhang, Jinshan Pan, Daoye Wang +5
Deblurring can not only provide visually more pleasant pictures and make photography more convenient, but also can improve the performance of objection detection as well as trackin…
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…