Abstract Graph Convolutional Network (GCN) which models the potential relationship
between non-Euclidean spatial data has attracted researchers' attention in deep learning in …

The promise of active learning (AL) is to reduce labelling costs by selecting the most
valuable examples to annotate from a pool of unlabelled data. Identifying these examples is …

While deep learning succeeds in a wide range of tasks, it highly depends on the massive
collection of annotated data which is expensive and time-consuming. To lower the cost of …

Deep learning (DL) techniques have shown remarkable progress in remotely sensed
hyperspectral image (HSI) classification tasks. The performance of DL-based models highly …

Recently, few-shot action recognition receives increasing attention and achieves remarkable
progress. However, previous methods mainly rely on limited unimodal data (eg, RGB …

Active learning (AL) is to design label-efficient algorithms by labeling the most
representative samples. It reduces annotation cost and attracts increasing attention from the …

Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

The existing pose estimation approaches often require a large number of annotated images
to attain good estimation performance, which are laborious to acquire. To reduce the human …

Labeling a large set of data is expensive. Active learning aims to tackle this problem by
asking to annotate only the most informative data from the unlabeled set. We propose a …