The performance of deep neural networks improves with more annotated data. The problem
is that the budget for annotation is limited. One solution to this is active learning, where a …

Despite the substantial progress of active learning for image recognition, there still lacks an
instance-level active learning method specified for object detection. In this paper, we …

The existing active learning methods select the samples by evaluating the sample's
uncertainty or its effect on the diversity of labeled datasets based on different task-specific or …

Active learning (AL) combines data labeling and model training to minimize the labeling cost
by prioritizing the selection of high value data that can best improve model performance. In …

Current approaches for fine-grained recognition do the following: First, recruit experts to
annotate a dataset of images, optionally also collecting more structured data in the form of …

Often, labeling large amount of data is challenging due to high labeling cost limiting the
application domain of deep learning techniques. Active learning (AL) tackles this by …

Active learning is to design label-efficient algorithms by sampling the most representative
samples to be labeled by an oracle. In this paper, we propose a state relabeling adversarial …

We propose a semi-automatic method to obtain foreground object masks for a large set of
related images. We develop a stagewise active approach to propagation: in each stage, we …

Machine learning can provide deep insights into data, allowing machines to make high-
quality predictions and having been widely used in real-world applications, such as text …

How can we find a general way to choose the most suitable samples for training a classifier?
Even with very limited prior information? Active learning, which can be regarded as an …