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20192021
most citedFew-Shot Class-Incremental Learning

15 citations · 18 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CV2021

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…

cs.CV20212 cited

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…

cs.CV20211 cited

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…

cs.CV2021

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…

cs.CV202015 cited

Few-Shot Class-Incremental Learning

Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang +3

The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical f…

cs.CV2019

Beyond Universal Person Re-ID Attack

Wenjie Ding, Xing Wei, Rongrong Ji +3

Deep learning-based person re-identification (Re-ID) has made great progress and achieved high performance recently. In this paper, we make the first attempt to examine the vulnera…