Active learning, a label-efficient paradigm, empowers models to interactively query an oracle
for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem …

Impressive performance on point cloud semantic segmentation has been achieved by fully-
supervised methods with large amounts of labelled data. As it is labour-intensive to acquire …

To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is
a promising solution that learns to select only a small portion of unlabeled data to annotate …

Existing work shows that 3D point clouds produce only about a 4% drop in semantic
segmentation even at 1% random point annotation, which inspires us to further explore how …

Manual annotation of every point in a point cloud is a costly and labor-intensive process.
While weakly supervised point cloud semantic segmentation (WSPCSS) with sparse …

Weak supervision has proven to be an effective strategy for reducing the burden of
annotating semantic segmentation tasks in 3D space. However, unconstrained or heuristic …

Reliance on vast annotations to achieve leading performance severely restricts the
practicality of large-scale point cloud semantic segmentation. For the purpose of reducing …

A metaverse is composed of a physical-space and virtual-space, with the aim of having
users in both the virtual reality and the real world experience. Prioritization is essential, but it …

Textbook Question Answering (TQA) task requires answering questions by reasoning based
on both the given diagrams and text context. There are mainly two challenges for the task …

Federated affective computing, which deploys traditional affective computing in a distributed
framework, achieves a trade-off between privacy and utility, and offers a wide variety of …