1k citations · 1.6k across the 21 of their papers we have counts for
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MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection
Dong Liang, Jing-Wei Zhang, Ying-Peng Tang +1
Recent aerial object detection models rely on a large amount of labeled training data, which requires unaffordable manual labeling costs in large aerial scenes with dense objects.…
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation
Feng Sun, Ming-Kun Xie, Sheng-Jun Huang
In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels an…
Active Learning for Open-set Annotation
Kun-Peng Ning, Xun Zhao, Yu Li +1
Existing active learning studies typically work in the closed-set setting by assuming that all data examples to be labeled are drawn from known classes. However, in real annotation…
Can Adversarial Training Be Manipulated By Non-Robust Features?
Lue Tao, Lei Feng, Hongxin Wei +3
Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. This defense ability…